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CVPR2024 6

Unmixing Diffusion for Self-Supervised Hyperspectral Image Denoising

Haijin Zeng, Jiezhang Cao, Kai Zhang, Yongyong Chen, Hiep Luong, Wilfried Philips

Hyperspectral images (HSIs) have extensive applications in various fields such as medicine agriculture and industry. Nevertheless acquiring high signal-to-noise ratio HSI poses a challenge due to narrow-band spectral filtering. Consequently the importance of HSI denoising is substantial especially for snapshot hyperspectral imaging technology. While most previous HSI denoising methods are supervised creating supervised training datasets for the diverse scenes hyperspectral cameras and scan parameters is impractical. In this work we present Diff-Unmix a self-supervised denoising method for HSI using diffusion denoising generative models. Specifically Diff-Unmix addresses the challenge of recovering noise-degraded HSI through a fusion of Spectral Unmixing and conditional abundance generation. Firstly it employs a learnable block-based spectral unmixing strategy complemented by a pure transformer-based backbone. Then we introduce a self-supervised generative diffusion network to enhance abundance maps from the spectral unmixing block. This network reconstructs noise-free Unmixing probability distributions effectively mitigating noise-induced degradations within these components. Finally the reconstructed HSI is reconstructed through unmixing reconstruction by blending the diffusion-adjusted abundance map with the spectral endmembers. Experimental results on both simulated and real-world noisy datasets show that Diff-Unmix achieves state-of-the-art performance.

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Seeing the World through Your Eyes

http://arxiv.org/abs/2306.09348

Hadi Alzayer, Kevin Zhang, Brandon Feng, Christopher A. Metzler, Jia-Bin Huang

The reflective nature of the human eye is an under-appreciated source of information about what the world around us looks like. By imaging the eyes of a moving person we capture multiple views of a scene outside the camera's direct line of sight through the reflections in the eyes. In this paper we reconstruct a radiance field beyond the camera's line of sight using portrait images containing eye reflections. This task is challenging due to 1) the difficulty of accurately estimating eye poses and 2) the entangled appearance of the iris textures and the scene reflections. To address these our method jointly optimizes the cornea poses the radiance field depicting the scene and the observer's eye iris texture. We further present a regularization prior on the iris texture to improve scene reconstruction quality. Through various experiments on synthetic and real-world captures featuring people with varied eye colors and lighting conditions we demonstrate the feasibility of our approach to recover the radiance field using cornea reflections.

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DPMesh: Exploiting Diffusion Prior for Occluded Human Mesh Recovery

http://arxiv.org/abs/2404.01424

Yixuan Zhu, Ao Li, Yansong Tang, Wenliang Zhao, Jie Zhou, Jiwen Lu

The recovery of occluded human meshes poses challenges for current methods due to the difficulty in extracting effective image features under severe occlusion. In this paper we introduce DPMesh an innovative framework for occluded human mesh recovery that capitalizes on the profound knowledge about object structure and spatial relationships embedded in a pre-trained text-to-image diffusion model. Unlike previous methods reliant on conventional backbones for vanilla feature extraction DPMesh seamlessly integrates the pre-trained denoising U-Net with potent priors as its image backbone and performs a single-step inference to provide occlusion-aware information. To enhance the perception capability for occluded poses DPMesh incorporates judicious guidance via condition injection which produces effective controls from 2D observations for the denoising U-Net. Furthermore we explore a dedicated noisy key-point reasoning approach to mitigate disturbances arising from occlusion and crowded scenarios. This strategy fully unleashes the perceptual capability of the diffusion prior thereby enhancing accuracy. Extensive quantitative and qualitative experiments affirm the efficacy of our framework as we outperform state-of-the-art methods on both occlusion-specific and standard datasets underscoring its ability to achieve precise and robust 3D human mesh recovery particularly in challenging scenarios involving occlusion and crowded scenes. Code is available at https://github.com/EternalEvan/DPMesh.

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Ungeneralizable Examples

http://arxiv.org/abs/2404.14016

Jingwen Ye, Xinchao Wang

The training of contemporary deep learning models heavily relies on publicly available data posing a risk of unauthorized access to online data and raising concerns about data privacy. Current approaches to creating unlearnable data involve incorporating small specially designed noises but these methods strictly limit data usability overlooking its potential usage in authorized scenarios. In this paper we extend the concept of unlearnable data to conditional data learnability and introduce UnGeneralizable Examples (UGEs). UGEs exhibit learnability for authorized users while maintaining unlearnability for potential hackers. The protector defines the authorized network and optimizes UGEs to match the gradients of the original data and its ungeneralizable version ensuring learnability. To prevent unauthorized learning UGEs are trained by maximizing a designated distance loss in a common feature space. Additionally to further safeguard the authorized side from potential attacks we introduce additional undistillation optimization. Experimental results on multiple datasets and various networks demonstrate that the proposed UGEs framework preserves data usability while reducing training performance on hacker networks even under different types of attacks.

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LaneCPP: Continuous 3D Lane Detection using Physical Priors

Maximilian Pittner, Joel Janai, Alexandru P. Condurache

Monocular 3D lane detection has become a fundamental problem in the context of autonomous driving which comprises the tasks of finding the road surface and locating lane markings. One major challenge lies in a flexible but robust line representation capable of modeling complex lane structures while still avoiding unpredictable behavior. While previous methods rely on fully data-driven approaches we instead introduce a novel approach LaneCPP that uses a continuous 3D lane detection model leveraging physical prior knowledge about the lane structure and road geometry. While our sophisticated lane model is capable of modeling complex road structures it also shows robust behavior since physical constraints are incorporated by means of a regularization scheme that can be analytically applied to our parametric representation. Moreover we incorporate prior knowledge about the road geometry into the 3D feature space by modeling geometry-aware spatial features guiding the network to learn an internal road surface representation. In our experiments we show the benefits of our contributions and prove the meaningfulness of using priors to make 3D lane detection more robust. The results show that LaneCPP achieves state-of-the-art performance in terms of F-Score and geometric errors.

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CityDreamer: Compositional Generative Model of Unbounded 3D Cities

http://arxiv.org/abs/2309.00610

Haozhe Xie, Zhaoxi Chen, Fangzhou Hong, Ziwei Liu

3D city generation is a desirable yet challenging task since humans are more sensitive to structural distortions in urban environments. Additionally generating 3D cities is more complex than 3D natural scenes since buildings as objects of the same class exhibit a wider range of appearances compared to the relatively consistent appearance of objects like trees in natural scenes. To address these challenges we propose CityDreamer a compositional generative model designed specifically for unbounded 3D cities. Our key insight is that 3D city generation should be a composition of different types of neural fields: 1) various building instances and 2) background stuff such as roads and green lands. Specifically we adopt the bird's eye view scene representation and employ a volumetric render for both instance-oriented and stuff-oriented neural fields. The generative hash grid and periodic positional embedding are tailored as scene parameterization to suit the distinct characteristics of building instances and background stuff. Furthermore we contribute a suite of CityGen Datasets including OSM and GoogleEarth which comprises a vast amount of real-world city imagery to enhance the realism of the generated 3D cities both in their layouts and appearances. CityDreamer achieves state-of-the-art performance not only in generating realistic 3D cities but also in localized editing within the generated cities.

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HEAL-SWIN: A Vision Transformer On The Sphere

Oscar Carlsson, Jan E. Gerken, Hampus Linander, Heiner Spieß, Fredrik Ohlsson, Christoffer Petersson, Daniel Persson

High-resolution wide-angle fisheye images are becoming more and more important for robotics applications such as autonomous driving. However using ordinary convolutional neural networks or vision transformers on this data is problematic due to projection and distortion losses introduced when projecting to a rectangular grid on the plane. We introduce the HEAL-SWIN transformer which combines the highly uniform Hierarchical Equal Area iso-Latitude Pixelation (HEALPix) grid used in astrophysics and cosmology with the Hierarchical Shifted-Window (SWIN) transformer to yield an efficient and flexible model capable of training on high-resolution distortion-free spherical data. In HEAL-SWIN the nested structure of the HEALPix grid is used to perform the patching and windowing operations of the SWIN transformer enabling the network to process spherical representations with minimal computational overhead. We demonstrate the superior performance of our model on both synthetic and real automotive datasets as well as a selection of other image datasets for semantic segmentation depth regression and classification tasks. Our code is publicly available.

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3D Paintbrush: Local Stylization of 3D Shapes with Cascaded Score Distillation

http://arxiv.org/abs/2311.09571

Dale Decatur, Itai Lang, Kfir Aberman, Rana Hanocka

We present 3D Paintbrush a technique for automatically texturing local semantic regions on meshes via text descriptions. Our method is designed to operate directly on meshes producing texture maps which seamlessly integrate into standard graphics pipelines. We opt to simultaneously produce a localization map (to specify the edit region) and a texture map which conforms to it. This approach improves the quality of both the localization and the stylization. To enhance the details and resolution of the textured area we leverage multiple stages of a cascaded diffusion model to supervise our local editing technique with generative priors learned from images at different resolutions. Our technique referred to as Cascaded Score Distillation (CSD) simultaneously distills scores at multiple resolutions in a cascaded fashion enabling control over both the granularity and global understanding of the supervision. We demonstrate the effectiveness of 3D Paintbrush to locally texture different semantic regions on a variety of shapes.

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Test-Time Linear Out-of-Distribution Detection

Ke Fan, Tong Liu, Xingyu Qiu, Yikai Wang, Lian Huai, Zeyu Shangguan, Shuang Gou, Fengjian Liu, Yuqian Fu, Yanwei Fu, Xingqun Jiang

Out-of-Distribution (OOD) detection aims to address the excessive confidence prediction by neural networks by triggering an alert when the input sample deviates significantly from the training distribution (in-distribution) indicating that the output may not be reliable. Current OOD detection approaches explore all kinds of cues to identify OOD data such as finding irregular patterns in the feature space logit space gradient space or the raw image space. Surprisingly we observe a linear trend between the OOD score produced by current OOD detection algorithms and the network features on several datasets. We conduct a thorough investigation theoretically and empirically to analyze and understand the meaning of such a linear trend in OOD detection. This paper proposes a Robust Test-time Linear method (RTL) to utilize such linear trends like a `free lunch' when we have a batch of data to perform OOD detection. By using a simple linear regression as a test time adaptation we can make a more precise OOD prediction. We further propose an online variant of the proposed method which achieves promising performance and is more practical for real applications. Theoretical analysis is given to prove the effectiveness of our methods. Extensive experiments on several OOD datasets show the efficacy of RTL for OOD detection tasks significantly improving the results of base OOD detectors. Project will be available at https://github.com/kfan21/RTL.

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Guided Slot Attention for Unsupervised Video Object Segmentation

http://arxiv.org/abs/2303.08314

Minhyeok Lee, Suhwan Cho, Dogyoon Lee, Chaewon Park, Jungho Lee, Sangyoun Lee

Unsupervised video object segmentation aims to segment the most prominent object in a video sequence. However the existence of complex backgrounds and multiple foreground objects make this task challenging. To address this issue we propose a guided slot attention network to reinforce spatial structural information and obtain better foreground-background separation. The foreground and background slots which are initialized with query guidance are iteratively refined based on interactions with template information. Furthermore to improve slot-template interaction and effectively fuse global and local features in the target and reference frames K-nearest neighbors filtering and a feature aggregation transformer are introduced. The proposed model achieves state-of-the-art performance on two popular datasets. Additionally we demonstrate the robustness of the proposed model in challenging scenes through various comparative experiments.

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Unsupervised Blind Image Deblurring Based on Self-Enhancement

Lufei Chen, Xiangpeng Tian, Shuhua Xiong, Yinjie Lei, Chao Ren

Significant progress in image deblurring has been achieved by deep learning methods especially the remarkable performance of supervised models on paired synthetic data. However real-world quality degradation is more complex than synthetic datasets and acquiring paired data in real-world scenarios poses significant challenges. To address these challenges we propose a novel unsupervised image deblurring framework based on self-enhancement. The framework progressively generates improved pseudo-sharp and blurry image pairs without the need for real paired datasets and the generated image pairs with higher qualities can be used to enhance the performance of the reconstructor. To ensure the generated blurry images are closer to the real blurry images we propose a novel re-degradation principal component consistency loss which enforces the principal components of the generated low-quality images to be similar to those of re-degraded images from the original sharp ones. Furthermore we introduce the self-enhancement strategy that significantly improves deblurring performance without increasing the computational complexity of network during inference. Through extensive experiments on multiple real-world blurry datasets we demonstrate the superiority of our approach over other state-of-the-art unsupervised methods.

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Action Detection via an Image Diffusion Process

http://arxiv.org/abs/2404.01051

Lin Geng Foo, Tianjiao Li, Hossein Rahmani, Jun Liu

Action detection aims to localize the starting and ending points of action instances in untrimmed videos and predict the classes of those instances. In this paper we make the observation that the outputs of the action detection task can be formulated as images. Thus from a novel perspective we tackle action detection via a three-image generation process to generate starting point ending point and action-class predictions as images via our proposed Action Detection Image Diffusion (ADI-Diff) framework. Furthermore since our images differ from natural images and exhibit special properties we further explore a Discrete Action-Detection Diffusion Process and a Row-Column Transformer design to better handle their processing. Our ADI-Diff framework achieves state-of-the-art results on two widely-used datasets.

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Programmable Motion Generation for Open-Set Motion Control Tasks

http://arxiv.org/abs/2405.19283

Hanchao Liu, Xiaohang Zhan, Shaoli Huang, Tai-Jiang Mu, Ying Shan

Character animation in real-world scenarios necessitates a variety of constraints such as trajectories key-frames interactions etc. Existing methodologies typically treat single or a finite set of these constraint(s) as separate control tasks. These methods are often specialized and the tasks they address are rarely extendable or customizable. We categorize these as solutions to the close-set motion control problem. In response to the complexity of practical motion control we propose and attempt to solve the open-set motion control problem. This problem is characterized by an open and fully customizable set of motion control tasks. To address this we introduce a new paradigm programmable motion generation. In this paradigm any given motion control task is broken down into a combination of atomic constraints. These constraints are then programmed into an error function that quantifies the degree to which a motion sequence adheres to them. We utilize a pre-trained motion generation model and optimize its latent code to minimize the error function of the generated motion. Consequently the generated motion not only inherits the prior of the generative model but also satisfies the requirements of the compounded constraints. Our experiments demonstrate that our approach can generate high-quality motions when addressing a wide range of unseen tasks. These tasks encompass motion control by motion dynamics geometric constraints physical laws interactions with scenes objects or the character's own body parts etc. All of these are achieved in a unified approach without the need for ad-hoc paired training data collection or specialized network designs. During the programming of novel tasks we observed the emergence of new skills beyond those of the prior model. With the assistance of large language models we also achieved automatic programming. We hope that this work will pave the way for the motion control of general AI agents.

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SCE-MAE: Selective Correspondence Enhancement with Masked Autoencoder for Self-Supervised Landmark Estimation

Kejia Yin, Varshanth Rao, Ruowei Jiang, Xudong Liu, Parham Aarabi, David B. Lindell

Self-supervised landmark estimation is a challenging task that demands the formation of locally distinct feature representations to identify sparse facial landmarks in the absence of annotated data. To tackle this task existing state-of-the-art (SOTA) methods (1) extract coarse features from backbones that are trained with instance-level self-supervised learning (SSL) paradigms which neglect the dense prediction nature of the task (2) aggregate them into memory-intensive hypercolumn formations and (3) supervise lightweight projector networks to naively establish full local correspondences among all pairs of spatial features. In this paper we introduce SCE-MAE a framework that (1) leverages the MAE [??] a region-level SSL method that naturally better suits the landmark prediction task (2) operates on the vanilla feature map instead of on expensive hypercolumns and (3) employs a Correspondence Approximation and Refinement Block (CARB) that utilizes a simple density peak clustering algorithm and our proposed Locality-Constrained Repellence Loss to directly hone only select local correspondences. We demonstrate through extensive experiments that SCE-MAE is highly effective and robust outperforming existing SOTA methods by large margins of 20%-44% on the landmark matching and 9%-15% on the landmark detection tasks.

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LAKE-RED: Camouflaged Images Generation by Latent Background Knowledge Retrieval-Augmented Diffusion

Pancheng Zhao, Peng Xu, Pengda Qin, Deng-Ping Fan, Zhicheng Zhang, Guoli Jia, Bowen Zhou, Jufeng Yang

Camouflaged vision perception is an important vision task with numerous practical applications. Due to the expensive collection and labeling costs this community struggles with a major bottleneck that the species category of its datasets is limited to a small number of object species. However the existing camouflaged generation methods require specifying the background manually thus failing to extend the camouflaged sample diversity in a low-cost manner. In this paper we propose a Latent Background Knowledge Retrieval-Augmented Diffusion (LAKE-RED) for camouflaged image generation. To our knowledge our contributions mainly include: (1) For the first time we propose a camouflaged generation paradigm that does not need to receive any background inputs. (2) Our LAKE-RED is the first knowledge retrieval-augmented method with interpretability for camouflaged generation in which we propose an idea that knowledge retrieval and reasoning enhancement are separated explicitly to alleviate the task-specific challenges. Moreover our method is not restricted to specific foreground targets or backgrounds offering a potential for extending camouflaged vision perception to more diverse domains. (3) Experimental results demonstrate that our method outperforms the existing approaches generating more realistic camouflage images.

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TIGER: Time-Varying Denoising Model for 3D Point Cloud Generation with Diffusion Process

Zhiyuan Ren, Minchul Kim, Feng Liu, Xiaoming Liu

Recently diffusion models have emerged as a new powerful generative method for 3D point cloud generation tasks. However few works study the effect of the architecture of the diffusion model in the 3D point cloud resorting to the typical UNet model developed for 2D images. Inspired by the wide adoption of Transformers we study the complementary role of convolution (from UNet) and attention (from Transformers). We discover that their respective importance change according to the timestep in the diffusion process. At early stage attention has an outsized influence because Transformers are found to generate the overall shape more quickly and at later stages when adding fine detail convolution starts having a larger impact on the generated point cloud's local surface quality. In light of this observation we propose a time-varying two-stream denoising model combined with convolution layers and transformer blocks. We generate an optimizable mask from each timestep to reweigh global and local features obtaining time-varying fused features. Experimentally we demonstrate that our proposed method quantitatively outperforms other state-of-the-art methods regarding visual quality and diversity. Code is avaiable github.com/Zhiyuan-R/Tiger-Time-varying-Diffusion-Model-for-Point-Cloud-Generation.

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ConTex-Human: Free-View Rendering of Human from a Single Image with Texture-Consistent Synthesis

Xiangjun Gao, Xiaoyu Li, Chaopeng Zhang, Qi Zhang, Yanpei Cao, Ying Shan, Long Quan

In this work we propose a method to address the challenge of rendering a 3D human from a single image in a free-view manner. Some existing approaches could achieve this by using generalizable pixel-aligned implicit fields to reconstruct a textured mesh of a human or by employing a 2D diffusion model as guidance with the Score Distillation Sampling (SDS) method to lift the 2D image into 3D space. However a generalizable implicit field often results in an over-smooth texture field while the SDS method tends to lead to a texture-inconsistent novel view with the input image. In this paper we introduce a texture-consistent back view synthesis method that could transfer the reference image content to the back view through depth-guided mutual self-attention. With this method we could achieve high-fidelity and texture-consistent human rendering from a single image. Moreover to alleviate the color distortion that occurs in the side region we propose a visibility-aware patch consistency regularization combined with the synthesized back view texture. Experiments conducted on both real and synthetic data demonstrate the effectiveness of our method and show that our approach outperforms previous baseline methods.

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UFineBench: Towards Text-based Person Retrieval with Ultra-fine Granularity

http://arxiv.org/abs/2312.03441

Jialong Zuo, Hanyu Zhou, Ying Nie, Feng Zhang, Tianyu Guo, Nong Sang, Yunhe Wang, Changxin Gao

Existing text-based person retrieval datasets often have relatively coarse-grained text annotations. This hinders the model to comprehend the fine-grained semantics of query texts in real scenarios. To address this problem we contribute a new benchmark named UFineBench for text-based person retrieval with ultra-fine granularity. Firstly we construct a new dataset named UFine6926. We collect a large number of person images and manually annotate each image with two detailed textual descriptions averaging 80.8 words each. The average word count is three to four times that of the previous datasets. In addition of standard in-domain evaluation we also propose a special evaluation paradigm more representative of real scenarios. It contains a new evaluation set with cross domains cross textual granularity and cross textual styles named UFine3C and a new evaluation metric for accurately measuring retrieval ability named mean Similarity Distribution (mSD). Moreover we propose CFAM a more efficient algorithm especially designed for text-based person retrieval with ultra fine-grained texts. It achieves fine granularity mining by adopting a shared cross-modal granularity decoder and hard negative match mechanism. With standard in-domain evaluation CFAM establishes competitive performance across various datasets especially on our ultra fine-grained UFine6926. Furthermore by evaluating on UFine3C we demonstrate that training on our UFine6926 significantly improves generalization to real scenarios compared with other coarse-grained datasets. The dataset and code will be made publicly available at https://github.com/Zplusdragon/UFineBench.

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Efficient Hyperparameter Optimization with Adaptive Fidelity Identification

Jiantong Jiang, Zeyi Wen, Atif Mansoor, Ajmal Mian

Hyperparameter Optimization and Neural Architecture Search are powerful in attaining state-of-the-art machine learning models with Bayesian Optimization (BO) standing out as a mainstream method. Extending BO into the multi-fidelity setting has been an emerging research topic in this field but faces the challenge of determining an appropriate fidelity for each hyperparameter configuration to fit the surrogate model. To tackle the challenge we propose a multi-fidelity BO method named FastBO which excels in adaptively deciding the fidelity for each configuration and providing strong performance while ensuring efficient resource usage. These advantages are achieved through our proposed techniques based on the concepts of efficient point and saturation point for each configuration which can be obtained from the empirical learning curve of the configuration estimated from early observations. Extensive experiments demonstrate FastBO's superior anytime performance and efficiency in identifying high-quality configurations and architectures. We also show that our method provides a way to extend any single-fidelity method to the multi-fidelity setting highlighting the wide applicability of our approach.

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ASH: Animatable Gaussian Splats for Efficient and Photoreal Human Rendering

http://arxiv.org/abs/2312.05941

Haokai Pang, Heming Zhu, Adam Kortylewski, Christian Theobalt, Marc Habermann

Real-time rendering of photorealistic and controllable human avatars stands as a cornerstone in Computer Vision and Graphics. While recent advances in neural implicit rendering have unlocked unprecedented photorealism for digital avatars real-time performance has mostly been demonstrated for static scenes only. To address this we propose ASH an animatable Gaussian splatting approach for photorealistic rendering of dynamic humans in real time. We parameterize the clothed human as animatable 3D Gaussians which can be efficiently splatted into image space to generate the final rendering. However naively learning the Gaussian parameters in 3D space poses a severe challenge in terms of compute. Instead we attach the Gaussians onto a deformable character model and learn their parameters in 2D texture space which allows leveraging efficient 2D convolutional architectures that easily scale with the required number of Gaussians. We benchmark ASH with competing methods on pose-controllable avatars demonstrating that our method outperforms existing real-time methods by a large margin and shows comparable or even better results than offline methods.

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Focus on Hiders: Exploring Hidden Threats for Enhancing Adversarial Training

http://arxiv.org/abs/2312.07067

Qian Li, Yuxiao Hu, Yinpeng Dong, Dongxiao Zhang, Yuntian Chen

Adversarial training is often formulated as a min-max problem however concentrating only on the worst adversarial examples causes alternating repetitive confusion of the model i.e. previously defended or correctly classified samples are not defensible or accurately classifiable in subsequent adversarial training. We characterize such non-ignorable samples as "hiders" which reveal the hidden high-risk regions within the secure area obtained through adversarial training and prevent the model from finding the real worst cases. We demand the model to prevent hiders when defending against adversarial examples for improving accuracy and robustness simultaneously. By rethinking and redefining the min-max optimization problem for adversarial training we propose a generalized adversarial training algorithm called Hider-Focused Adversarial Training (HFAT). HFAT introduces the iterative evolution optimization strategy to simplify the optimization problem and employs an auxiliary model to reveal hiders effectively combining the optimization directions of standard adversarial training and prevention hiders. Furthermore we introduce an adaptive weighting mechanism that facilitates the model in adaptively adjusting its focus between adversarial examples and hiders during different training periods. We demonstrate the effectiveness of our method based on extensive experiments and ensure that HFAT can provide higher robustness and accuracy. We will release the source code upon publication.

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ArtAdapter: Text-to-Image Style Transfer using Multi-Level Style Encoder and Explicit Adaptation

http://arxiv.org/abs/2312.02109

Dar-Yen Chen, Hamish Tennent, Ching-Wen Hsu

This work introduces ArtAdapter a transformative text-to-image (T2I) style transfer framework that transcends traditional limitations of color brushstrokes and object shape capturing high-level style elements such as composition and distinctive artistic expression. The integration of a multi-level style encoder with our proposed explicit adaptation mechanism enables ArtAdapter to achieve unprecedented fidelity in style transfer ensuring close alignment with textual descriptions. Additionally the incorporation of an Auxiliary Content Adapter (ACA) effectively separates content from style alleviating the borrowing of content from style references. Moreover our novel fast finetuning approach could further enhance zero-shot style representation while mitigating the risk of overfitting. Comprehensive evaluations confirm that ArtAdapter surpasses current state-of-the-art methods.

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GoodSAM: Bridging Domain and Capacity Gaps via Segment Anything Model for Distortion-aware Panoramic Semantic Segmentation

http://arxiv.org/abs/2403.16370

Weiming Zhang, Yexin Liu, Xu Zheng, Lin Wang

This paper tackles a novel yet challenging problem: how to transfer knowledge from the emerging Segment Anything Model (SAM) -- which reveals impressive zero-shot instance segmentation capacity -- to learn a compact panoramic semantic segmentation model i.e. student without requiring any labeled data. This poses considerable challenges due to SAM's inability to provide semantic labels and the large capacity gap between SAM and the student. To this end we propose a novel framework called GoodSAM that introduces a teacher assistant (TA) to provide semantic information integrated with SAM to generate ensemble logits to achieve knowledge transfer. Specifically we propose a Distortion-Aware Rectification (DAR) module that first addresses the distortion problem of panoramic images by imposing prediction-level consistency and boundary enhancement. This subtly enhances TA's prediction capacity on panoramic images. DAR then incorporates a cross-task complementary fusion block to adaptively merge the predictions of SAM and TA to obtain more reliable ensemble logits. Moreover we introduce a Multi-level Knowledge Adaptation (MKA) module to efficiently transfer the multi-level feature knowledge from TA and ensemble logits to learn a compact student model. Extensive experiments on two benchmarks show that our GoodSAM achieves a remarkable +3.75% mIoU improvement over the state-of-the-art (SOTA) domain adaptation methods e.g. [41]. Also our most lightweight model achieves comparable performance to the SOTA methods with only 3.7M parameters.

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DYSON: Dynamic Feature Space Self-Organization for Online Task-Free Class Incremental Learning

Yuhang He, Yingjie Chen, Yuhan Jin, Songlin Dong, Xing Wei, Yihong Gong

In this paper we focus on a challenging Online Task-Free Class Incremental Learning (OTFCIL) problem. Different from the existing methods that continuously learn the feature space from data streams we propose a novel compute-and-align paradigm for the OTFCIL. It first computes an optimal geometry i.e. the class prototype distribution for classifying existing classes and updates it when new classes emerge and then trains a DNN model by aligning its feature space to the optimal geometry. To this end we develop a novel Dynamic Neural Collapse (DNC) algorithm to compute and update the optimal geometry. The DNC expands the geometry when new classes emerge without loss of the geometry optimality and guarantees the drift distance of old class prototypes with an explicit upper bound. Then we propose a novel Dynamic feature space Self-Organization (DYSON) method containing three major components including 1) a feature extractor 2) a Dynamic Feature-Geometry Alignment (DFGA) module aligning the feature space to the optimal geometry computed by DNC and 3) a training-free class-incremental classifier derived from the DNC geometry. Experimental comparison results on four benchmark datasets including CIFAR10 CIFAR100 CUB200 and CoRe50 demonstrate the efficiency and superiority of the DYSON method. The source code is provided in the supplementary material.

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Streaming Dense Video Captioning

http://arxiv.org/abs/2404.01297

Xingyi Zhou, Anurag Arnab, Shyamal Buch, Shen Yan, Austin Myers, Xuehan Xiong, Arsha Nagrani, Cordelia Schmid

An ideal model for dense video captioning -- predicting captions localized temporally in a video -- should be able to handle long input videos predict rich detailed textual descriptions and be able to produce outputs before processing the entire video. Current state-of-the-art models however process a fixed number of downsampled frames and make a single full prediction after seeing the whole video. We propose a streaming dense video captioning model that consists of two novel components: First we propose a new memory module based on clustering incoming tokens which can handle arbitrarily long videos as the memory is of a fixed size. Second we develop a streaming decoding algorithm that enables our model to make predictions before the entire video has been processed. Our model achieves this streaming ability and significantly improves the state-of-the-art on three dense video captioning benchmarks: ActivityNet YouCook2 and ViTT. Our code is released at https://github.com/google-research/scenic.

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Rethinking Inductive Biases for Surface Normal Estimation

http://arxiv.org/abs/2403.00712

Gwangbin Bae, Andrew J. Davison

Despite the growing demand for accurate surface normal estimation models existing methods use general-purpose dense prediction models adopting the same inductive biases as other tasks. In this paper we discuss the inductive biases needed for surface normal estimation and propose to (1) utilize the per-pixel ray direction and (2) encode the relationship between neighboring surface normals by learning their relative rotation. The proposed method can generate crisp - yet piecewise smooth - predictions for challenging in-the-wild images of arbitrary resolution and aspect ratio. Compared to a recent ViT-based state-of-the-art model our method shows a stronger generalization ability despite being trained on an orders of magnitude smaller dataset. The code is available at https://github.com/baegwangbin/DSINE.

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Event-based Structure-from-Orbit

http://arxiv.org/abs/2405.06216

Ethan Elms, Yasir Latif, Tae Ha Park, Tat-Jun Chin

Event sensors offer high temporal resolution visual sensing which makes them ideal for perceiving fast visual phenomena without suffering from motion blur. Certain applications in robotics and vision-based navigation require 3D perception of an object undergoing circular or spinning motion in front of a static camera such as recovering the angular velocity and shape of the object. The setting is equivalent to observing a static object with an orbiting camera. In this paper we propose event-based structure-from-orbit (eSfO) where the aim is to simultaneously reconstruct the 3D structure of a fast spinning object observed from a static event camera and recover the equivalent orbital motion of the camera. Our contributions are threefold: since state-of-the-art event feature trackers cannot handle periodic self-occlusion due to the spinning motion we develop a novel event feature tracker based on spatio-temporal clustering and data association that can better track the helical trajectories of valid features in the event data. The feature tracks are then fed to our novel factor graph-based structure-from-orbit back-end that calculates the orbital motion parameters (e.g. spin rate relative rotational axis) that minimize the reprojection error. For evaluation we produce a new event dataset of objects under spinning motion. Comparisons against ground truth indicate the efficacy of eSfO.

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LED: A Large-scale Real-world Paired Dataset for Event Camera Denoising

http://arxiv.org/abs/2405.19718

Yuxing Duan

Event camera has significant advantages in capturingdynamic scene information while being prone to noise interferenceparticularly in challenging conditions like lowthreshold and low illumination. However most existing researchfocuses on gentle situations hindering event cameraapplications in realistic complex scenarios. To tackle thislimitation and advance the field we construct a new pairedreal-world event denoising dataset (LED) including 3K sequenceswith 18K seconds of high-resolution (1200*680)event streams and showing three notable distinctions comparedto others: diverse noise levels and scenes largerscalewith high-resolution and high-quality GT. Specificallyit contains stepped parameters and varying illuminationwith diverse scenarios. Moreover based on theproperty of noise events inconsistency and signal eventsconsistency we propose a novel effective denoising framework(DED) using homogeneous dual events to generate theGT with better separating noise from the raw. Furthermorewe design a bio-inspired baseline leveraging Leaky-Integrate-and-Fire (LIF) neurons with dynamic thresholdsto realize accurate denoising. The experimental resultsdemonstrate that the remarkable performance of the proposedapproach on different datasets.The dataset and codeare at https://github.com/Yee-Sing/led.

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Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity

http://arxiv.org/abs/2405.16585

Yuhang Chen, Wenke Huang, Mang Ye

Federated learning (FL) has emerged as a new paradigm for privacy-preserving collaborative training. Under domain skew the current FL approaches are biased and face two fairness problems. 1) Parameter Update Conflict: data disparity among clients leads to varying parameter importance and inconsistent update directions. These two disparities cause important parameters to potentially be overwhelmed by unimportant ones of dominant updates. It consequently results in significant performance decreases for lower-performing clients. 2) Model Aggregation Bias: existing FL approaches introduce unfair weight allocation and neglect domain diversity. It leads to biased model convergence objective and distinct performance among domains. We discover a pronounced directional update consistency in Federated Learning and propose a novel framework to tackle above issues. First leveraging the discovered characteristic we selectively discard unimportant parameter updates to prevent updates from clients with lower performance overwhelmed by unimportant parameters resulting in fairer generalization performance. Second we propose a fair aggregation objective to prevent global model bias towards some domains ensuring that the global model continuously aligns with an unbiased model. The proposed method is generic and can be combined with other existing FL methods to enhance fairness. Comprehensive experiments on Digits and Office-Caltech demonstrate the high fairness and performance of our method.

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Activity-Biometrics: Person Identification from Daily Activities

Shehreen Azad, Yogesh Singh Rawat

In this work we study a novel problem which focuses on person identification while performing daily activities. Learning biometric features from RGB videos is challenging due to spatio-temporal complexity and presence of appearance biases such as clothing color and background. We propose ABNet a novel framework which leverages disentanglement of biometric and non-biometric features to perform effective person identification from daily activities. ABNet relies on a bias-less teacher to learn biometric features from RGB videos and explicitly disentangle non-biometric features with the help of biometric distortion. In addition ABNet also exploits activity prior for biometrics which is enabled by joint biometric and activity learning. We perform comprehensive evaluation of the proposed approach across five different datasets which are derived from existing activity recognition benchmarks. Furthermore we extensively compare ABNet with existing works in person identification and demonstrate its effectiveness for activity-based biometrics across all five datasets. The code and dataset can be accessed at: https://github.com/sacrcv/Activity-Biometrics/

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Z*: Zero-shot Style Transfer via Attention Reweighting

Yingying Deng, Xiangyu He, Fan Tang, Weiming Dong

Despite the remarkable progress in image style transfer formulating style in the context of art is inherently subjective and challenging. In contrast to existing methods this study shows that vanilla diffusion models can directly extract style information and seamlessly integrate the generative prior into the content image without retraining. Specifically we adopt dual denoising paths to represent content/style references in latent space and then guide the content image denoising process with style latent codes. We further reveal that the cross-attention mechanism in latent diffusion models tends to blend the content and style images resulting in stylized outputs that deviate from the original content image. To overcome this limitation we introduce a cross-attention reweighting strategy. Through theoretical analysis and experiments we demonstrate the effectiveness and superiority of the diffusion-based zero-shot style transfer via attention reweighting Z-STAR.

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HIG: Hierarchical Interlacement Graph Approach to Scene Graph Generation in Video Understanding

http://arxiv.org/abs/2312.03050

Trong-Thuan Nguyen, Pha Nguyen, Khoa Luu

Visual interactivity understanding within visual scenes presents a significant challenge in computer vision. Existing methods focus on complex interactivities while leveraging a simple relationship model. These methods however struggle with a diversity of appearance situation position interaction and relation in videos. This limitation hinders the ability to fully comprehend the interplay within the complex visual dynamics of subjects. In this paper we delve into interactivities understanding within visual content by deriving scene graph representations from dense interactivities among humans and objects. To achieve this goal we first present a new dataset containing Appearance-Situation-Position-Interaction-Relation predicates named ASPIRe offering an extensive collection of videos marked by a wide range of interactivities. Then we propose a new approach named Hierarchical Interlacement Graph (HIG) which leverages a unified layer and graph within a hierarchical structure to provide deep insights into scene changes across five distinct tasks. Our approach demonstrates superior performance to other methods through extensive experiments conducted in various scenarios.

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OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising

http://arxiv.org/abs/2404.02227

Haichao Zhang, Yi Xu, Hongsheng Lu, Takayuki Shimizu, Yun Fu

Trajectory prediction is fundamental in computer vision and autonomous driving particularly for understanding pedestrian behavior and enabling proactive decision-making. Existing approaches in this field often assume precise and complete observational data neglecting the challenges associated with out-of-view objects and the noise inherent in sensor data due to limited camera range physical obstructions and the absence of ground truth for denoised sensor data. Such oversights are critical safety concerns as they can result in missing essential non-visible objects. To bridge this gap we present a novel method for out-of-sight trajectory prediction that leverages a vision-positioning technique. Our approach denoises noisy sensor observations in an unsupervised manner and precisely maps sensor-based trajectories of out-of-sight objects into visual trajectories. This method has demonstrated state-of-the-art performance in out-of-sight noisy sensor trajectory denoising and prediction on the Vi-Fi and JRDB datasets. By enhancing trajectory prediction accuracy and addressing the challenges of out-of-sight objects our work significantly contributes to improving the safety and reliability of autonomous driving in complex environments. Our work represents the first initiative towards Out-Of-Sight Trajectory prediction (OOSTraj) setting a new benchmark for future research.

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FADES: Fair Disentanglement with Sensitive Relevance

Taeuk Jang, Xiaoqian Wang

Learning fair representation in deep learning is essential to mitigate discriminatory outcomes and enhance trustworthiness. However previous research has been commonly established on inappropriate assumptions prone to unrealistic counterfactuals and performance degradation. Although some proposed alternative approaches such as employing correlation-aware causal graphs or proxies for mutual information these methods are less practical and not applicable in general. In this work we propose FAir DisEntanglement with Sensitive relevance (FADES) a novel approach that leverages conditional mutual information from the information theory perspective to address these challenges. We employ sensitive relevant code to direct correlated information between target labels and sensitive attributes by imposing conditional independence allowing better separation of the features of interest in the latent space. Utilizing an intuitive disentangling approach FADES consistently achieves superior performance and fairness both quantitatively and qualitatively with its straightforward structure. Specifically the proposed method outperforms existing works in downstream classification and counterfactual generations on various benchmarks.

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Learning Continuous 3D Words for Text-to-Image Generation

http://arxiv.org/abs/2402.08654

Ta-Ying Cheng, Matheus Gadelha, Thibault Groueix, Matthew Fisher, Radomir Mech, Andrew Markham, Niki Trigoni

Current controls over diffusion models (e.g. through text or ControlNet) for image generation fall short in recognizing abstract continuous attributes like illumination direction or non-rigid shape change. In this paper we present an approach for allowing users of text-to-image models to have fine-grained control of several attributes in an image. We do this by engineering special sets of input tokens that can be transformed in a continuous manner we call them Continuous 3D Words. These attributes can for example be represented as sliders and applied jointly with text prompts for fine-grained control over image generation. Given only a single mesh and a rendering engine we show that our approach can be adopted to provide continuous user control over several 3D-aware attributes including time-of-day illumination bird wing orientation dollyzoom effect and object poses. Our method is capable of conditioning image creation with multiple Continuous 3D Words and text descriptions simultaneously while adding no overhead to the generative process.

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MarkovGen: Structured Prediction for Efficient Text-to-Image Generation

http://arxiv.org/abs/2308.10997

Sadeep Jayasumana, Daniel Glasner, Srikumar Ramalingam, Andreas Veit, Ayan Chakrabarti, Sanjiv Kumar

Modern text-to-image generation models produce high-quality images that are both photorealistic and faithful to the text prompts. However this quality comes at significant computational cost: nearly all of these models are iterative and require running sampling multiple times with large models. This iterative process is needed to ensure that different regions of the image are not only aligned with the text prompt but also compatible with each other. In this work we propose a light-weight approach to achieving this compatibility between different regions of an image using a Markov Random Field (MRF) model. We demonstrate the effectiveness of this method on top of the latent token-based Muse text-to-image model. The MRF richly encodes the compatibility among image tokens at different spatial locations to improve quality and significantly reduce the required number of Muse sampling steps. Inference with the MRF is significantly cheaper and its parameters can be quickly learned through back-propagation by modeling MRF inference as a differentiable neural-network layer. Our full model MarkovGen uses this proposed MRF model to both speed up Muse by 1.5xand produce higher quality images by decreasing undesirable image artifacts.

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Self-Supervised Class-Agnostic Motion Prediction with Spatial and Temporal Consistency Regularizations

http://arxiv.org/abs/2403.13261

Kewei Wang, Yizheng Wu, Jun Cen, Zhiyu Pan, Xingyi Li, Zhe Wang, Zhiguo Cao, Guosheng Lin

The perception of motion behavior in a dynamic environment holds significant importance for autonomous driving systems wherein class-agnostic motion prediction methods directly predict the motion of the entire point cloud. While most existing methods rely on fully-supervised learning the manual labeling of point cloud data is laborious and time-consuming. Therefore several annotation-efficient methods have been proposed to address this challenge. Although effective these methods rely on weak annotations or additional multi-modal data like images and the potential benefits inherent in the point cloud sequence are still underexplored. To this end we explore the feasibility of self-supervised motion prediction with only unlabeled LiDAR point clouds. Initially we employ an optimal transport solver to establish coarse correspondences between current and future point clouds as the coarse pseudo motion labels. Training models directly using such coarse labels leads to noticeable spatial and temporal prediction inconsistencies. To mitigate these issues we introduce three simple spatial and temporal regularization losses which facilitate the self-supervised training process effectively. Experimental results demonstrate the significant superiority of our approach over the state-of-the-art self-supervised methods. Code will be available.

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HashPoint: Accelerated Point Searching and Sampling for Neural Rendering

Jiahao Ma, Miaomiao Liu, David Ahmedt-Aristizabal, Chuong Nguyen

In this paper we address the problem of efficient point searching and sampling for volume neural rendering. Within this realm two typical approaches are employed: rasterization and ray tracing. The rasterization-based methods enable real-time rendering at the cost of increased memory and lower fidelity. In contrast the ray-tracing-based methods yield superior quality but demand longer rendering time. We solve this problem by our HashPoint method combining these two strategies leveraging rasterization for efficient point searching and sampling and ray marching for rendering. Our method optimizes point searching by rasterizing points within the camera's view organizing them in a hash table and facilitating rapid searches. Notably we accelerate the rendering process by adaptive sampling on the primary surface encountered by the ray. Our approach yields substantial speed-up for a range of state-of-the-art ray-tracing-based methods maintaining equivalent or superior accuracy across synthetic and real test datasets. The code will be available at https://jiahao-ma.github.io/hashpoint/

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MFP: Making Full Use of Probability Maps for Interactive Image Segmentation

http://arxiv.org/abs/2404.18448

Chaewon Lee, Seon-Ho Lee, Chang-Su Kim

In recent interactive segmentation algorithms previous probability maps are used as network input to help predictions in the current segmentation round. However despite the utilization of previous masks useful information contained in the probability maps is not well propagated to the current predictions. In this paper to overcome this limitation we propose a novel and effective algorithm for click-based interactive image segmentation called MFP which attempts to make full use of probability maps. We first modulate previous probability maps to enhance their representations of user-specified objects. Then we feed the modulated probability maps as additional input to the segmentation network. We implement the proposed MFP algorithm based on the ResNet-34 HRNet-18 and ViT-B backbones and assess the performance extensively on various datasets. It is demonstrated that MFP meaningfully outperforms the existing algorithms using identical backbones. The source codes are available at https://github.com/cwlee00/MFP.

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CAT: Exploiting Inter-Class Dynamics for Domain Adaptive Object Detection

http://arxiv.org/abs/2403.19278

Mikhail Kennerley, Jian-Gang Wang, Bharadwaj Veeravalli, Robby T. Tan

Domain adaptive object detection aims to adapt detection models to domains where annotated data is unavailable. Existing methods have been proposed to address the domain gap using the semi-supervised student-teacher framework. However a fundamental issue arises from the class imbalance in the labelled training set which can result in inaccurate pseudo-labels. The relationship between classes especially where one class is a majority and the other minority has a large impact on class bias. We propose Class-Aware Teacher (CAT) to address the class bias issue in the domain adaptation setting. In our work we approximate the class relationships with our Inter-Class Relation module (ICRm) and exploit it to reduce the bias within the model. In this way we are able to apply augmentations to highly related classes both inter- and intra-domain to boost the performance of minority classes while having minimal impact on majority classes. We further reduce the bias by implementing a class-relation weight to our classification loss. Experiments conducted on various datasets and ablation studies show that our method is able to address the class bias in the domain adaptation setting. On the Cityscapes ? Foggy Cityscapes dataset we attained a 52.5 mAP a substantial improvement over the 51.2 mAP achieved by the state-of-the-art method.

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StyLitGAN: Image-Based Relighting via Latent Control

Anand Bhattad, James Soole, D.A. Forsyth

We describe a novel method StyLitGAN for relighting and resurfacing images in the absence of labeled data. StyLitGAN generates images with realistic lighting effects including cast shadows soft shadows inter-reflections and glossy effects without the need for paired or CGI data. StyLitGAN uses an intrinsic image method to decompose an image followed by a search of the latent space of a pretrained StyleGAN to identify a set of directions. By prompting the model to fix one component (e.g. albedo) and vary another (e.g. shading) we generate relighted images by adding the identified directions to the latent style codes. Quantitative metrics of change in albedo and lighting diversity allow us to choose effective directions using a forward selection process. Qualitative evaluation confirms the effectiveness of our method.

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An Empirical Study of Scaling Law for Scene Text Recognition

Miao Rang, Zhenni Bi, Chuanjian Liu, Yunhe Wang, Kai Han

The laws of model size data volume computation and model performance have been extensively studied in the field of Natural Language Processing (NLP). However the scaling laws in Scene Text Recognition (STR) have not yet been investigated. To address this we conducted comprehensive studies that involved examining the correlations between performance and the scale of models data volume and computation in the field of text recognition. Conclusively the study demonstrates smooth power laws between performance and model size as well as training data volume when other influencing factors are held constant. Additionally we have constructed a large-scale dataset called REBU-Syn which comprises 6 million real samples and 18 million synthetic samples. Based on our scaling law and new dataset we have successfully trained a scene text recognition model achieving a new state-of-the-art on 6 common test benchmarks with a top-1 average accuracy of 97.42%. The models and dataset are publicly available at \href https://github.com/large-ocr-model/large-ocr-model.github.io large-ocr-model.github.io .

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Text2Loc: 3D Point Cloud Localization from Natural Language

http://arxiv.org/abs/2311.15977

Yan Xia, Letian Shi, Zifeng Ding, Joao F. Henriques, Daniel Cremers

We tackle the problem of 3D point cloud localization based on a few natural linguistic descriptions and introduce a novel neural network Text2Loc that fully interprets the semantic relationship between points and text. Text2Loc follows a coarse-to-fine localization pipeline: text-submap global place recognition followed by fine localization. In global place recognition relational dynamics among each textual hint are captured in a hierarchical transformer with max-pooling (HTM) whereas a balance between positive and negative pairs is maintained using text-submap contrastive learning. Moreover we propose a novel matching-free fine localization method to further refine the location predictions which completely removes the need for complicated text-instance matching and is lighter faster and more accurate than previous methods. Extensive experiments show that Text2Loc improves the localization accuracy by up to 2x over the state-of-the-art on the KITTI360Pose dataset. Our project page is publicly available at: https: //yan-xia.github.io/projects/text2loc/.

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SVDinsTN: A Tensor Network Paradigm for Efficient Structure Search from Regularized Modeling Perspective

http://arxiv.org/abs/2305.14912

Yu-Bang Zheng, Xi-Le Zhao, Junhua Zeng, Chao Li, Qibin Zhao, Heng-Chao Li, Ting-Zhu Huang

Tensor network (TN) representation is a powerful technique for computer vision and machine learning. TN structure search (TN-SS) aims to search for a customized structure to achieve a compact representation which is a challenging NP-hard problem. Recent "sampling-evaluation"-based methods require sampling an extensive collection of structures and evaluating them one by one resulting in prohibitively high computational costs. To address this issue we propose a novel TN paradigm named SVD-inspired TN decomposition (SVDinsTN) which allows us to efficiently solve the TN-SS problem from a regularized modeling perspective eliminating the repeated structure evaluations. To be specific by inserting a diagonal factor for each edge of the fully-connected TN SVDinsTN allows us to calculate TN cores and diagonal factors simultaneously with the factor sparsity revealing a compact TN structure. In theory we prove a convergence guarantee for the proposed method. Experimental results demonstrate that the proposed method achieves approximately 100 1000 times acceleration compared to the state-of-the-art TN-SS methods while maintaining a comparable level of representation ability.

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Decomposing Disease Descriptions for Enhanced Pathology Detection: A Multi-Aspect Vision-Language Pre-training Framework

http://arxiv.org/abs/2403.07636

Vu Minh Hieu Phan, Yutong Xie, Yuankai Qi, Lingqiao Liu, Liyang Liu, Bowen Zhang, Zhibin Liao, Qi Wu, Minh-Son To, Johan W. Verjans

Medical vision language pre-training (VLP) has emerged as a frontier of research enabling zero-shot pathological recognition by comparing the query image with the textual descriptions for each disease. Due to the complex semantics of biomedical texts current methods struggle to align medical images with key pathological findings in unstructured reports. This leads to the misalignment with the target disease's textual representation. In this paper we introduce a novel VLP framework designed to dissect disease descriptions into their fundamental aspects leveraging prior knowledge about the visual manifestations of pathologies. This is achieved by consulting a large language model and medical experts. Integrating a Transformer module our approach aligns an input image with the diverse elements of a disease generating aspect-centric image representations. By consolidating the matches from each aspect we improve the compatibility between an image and its associated disease. Additionally capitalizing on the aspect-oriented representations we present a dual-head Transformer tailored to process known and unknown diseases optimizing the comprehensive detection efficacy. Conducting experiments on seven downstream datasets ours improves the accuracy of recent methods by up to 8.56% and 17.26% for seen and unseen categories respectively. Our code is released at https://github.com/HieuPhan33/MAVL.

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MoMask: Generative Masked Modeling of 3D Human Motions

http://arxiv.org/abs/2312.00063

Chuan Guo, Yuxuan Mu, Muhammad Gohar Javed, Sen Wang, Li Cheng

We introduce MoMask a novel masked modeling framework for text-driven 3D human motion generation. In MoMask a hierarchical quantization scheme is employed to represent human motion as multi-layer discrete motion tokens with high-fidelity details. Starting at the base layer with a sequence of motion tokens obtained by vector quantization the residual tokens of increasing orders are derived and stored at the subsequent layers of the hierarchy. This is consequently followed by two distinct bidirectional transformers. For the base-layer motion tokens a Masked Transformer is designated to predict randomly masked motion tokens conditioned on text input at training stage. During generation (i.e. inference) stage starting from an empty sequence our Masked Transformer iteratively fills up the missing tokens; Subsequently a Residual Transformer learns to progressively predict the next-layer tokens based on the results from current layer. Extensive experiments demonstrate that MoMask outperforms the state-of-art methods on the text-to-motion generation task with an FID of 0.045 (vs e.g. 0.141 of T2M-GPT) on the HumanML3D dataset and 0.228 (vs 0.514) on KIT-ML respectively. MoMask can also be seamlessly applied in related tasks without further model fine-tuning such as text-guided temporal inpainting.

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Inverse Rendering of Glossy Objects via the Neural Plenoptic Function and Radiance Fields

http://arxiv.org/abs/2403.16224

Haoyuan Wang, Wenbo Hu, Lei Zhu, Rynson W.H. Lau

Inverse rendering aims at recovering both geometry and materials of objects. It provides a more compatible reconstruction for conventional rendering engines compared with the neural radiance fields (NeRFs). On the other hand existing NeRF-based inverse rendering methods cannot handle glossy objects with local light interactions well as they typically oversimplify the illumination as a 2D environmental map which assumes infinite lights only. Observing the superiority of NeRFs in recovering radiance fields we propose a novel 5D Neural Plenoptic Function (NeP) based on NeRFs and ray tracing such that more accurate lighting-object interactions can be formulated via the rendering equation. We also design a material-aware cone sampling strategy to efficiently integrate lights inside the BRDF lobes with the help of pre-filtered radiance fields. Our method has two stages: the geometry of the target object and the pre-filtered environmental radiance fields are reconstructed in the first stage and materials of the target object are estimated in the second stage with the proposed NeP and material-aware cone sampling strategy. Extensive experiments on the proposed real-world and synthetic datasets demonstrate that our method can reconstruct high-fidelity geometry/materials of challenging glossy objects with complex lighting interactions from nearby objects. Project webpage: https://whyy.site/paper/nep

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Split to Merge: Unifying Separated Modalities for Unsupervised Domain Adaptation

http://arxiv.org/abs/2403.06946

Xinyao Li, Yuke Li, Zhekai Du, Fengling Li, Ke Lu, Jingjing Li

Large vision-language models (VLMs) like CLIP have demonstrated good zero-shot learning performance in the unsupervised domain adaptation task. Yet most transfer approaches for VLMs focus on either the language or visual branches overlooking the nuanced interplay between both modalities. In this work we introduce a Unified Modality Separation (UniMoS) framework for unsupervised domain adaptation. Leveraging insights from modality gap studies we craft a nimble modality separation network that distinctly disentangles CLIP's features into language-associated and vision-associated components. Our proposed Modality-Ensemble Training (MET) method fosters the exchange of modality-agnostic information while maintaining modality-specific nuances. We align features across domains using a modality discriminator. Comprehensive evaluations on three benchmarks reveal our approach sets a new state-of-the-art with minimal computational costs. Code: https://github.com/TL-UESTC/UniMoS.

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Fitting Flats to Flats

Gabriel Dogadov, Ugo Finnendahl, Marc Alexa

Affine subspaces of Euclidean spaces are also referred to as flats. A standard task in computer vision or more generally in engineering and applied sciences is fitting a flat to a set of points which is commonly solved using the PCA. We generalize this technique to enable fitting a flat to a set of other flats possibly of varying dimensions based on representing the flats as squared distance fields. Compared to previous approaches such as Riemannian centers of mass in the manifold of affine Grassmannians our approach is conceptually much simpler and computationally more efficient yet offers desirable properties such as respecting symmetries and being equivariant to rigid transformations leading to more intuitive and useful results in practice. We demonstrate these claims in a number of synthetic experiments and a multi-view reconstruction task of line-like objects.

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Fusing Personal and Environmental Cues for Identification and Segmentation of First-Person Camera Wearers in Third-Person Views

Ziwei Zhao, Yuchen Wang, Chuhua Wang

As wearable cameras become more popular an important question emerges: how to identify camera wearers within the perspective of conventional static cameras. The drastic difference between first-person (egocentric) and third-person (exocentric) camera views makes this a challenging task. We present PersonEnvironmentNet (PEN) a framework designed to integrate information from both the individuals in the two views and geometric cues inferred from the background environment. To facilitate research in this direction we also present TF2023 a novel dataset comprising synchronized first-person and third-person views along with masks of camera wearers and labels associating these masks with the respective first-person views. In addition we propose a novel quantitative metric designed to measure a model's ability to comprehend the relationship between the two views. Our experiments reveal that PEN outperforms existing methods. The code and dataset are available at https://github.com/ziweizhao1993/PEN.

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Coupled Laplacian Eigenmaps for Locally-Aware 3D Rigid Point Cloud Matching

Matteo Bastico, Etienne Decencière, Laurent Corté, Yannick Tillier, David Ryckelynck

Point cloud matching a crucial technique in computer vision medical and robotics fields is primarily concerned with finding correspondences between pairs of point clouds or voxels. In some practical scenarios emphasizing local differences is crucial for accurately identifying a correct match thereby enhancing the overall robustness and reliability of the matching process. Commonly used shape descriptors have several limitations and often fail to provide meaningful local insights about the paired geometries. In this work we propose a new technique based on graph Laplacian eigenmaps to match point clouds by taking into account fine local structures. To deal with the order and sign ambiguity of Laplacian eigenmaps we introduce a new operator called Coupled Laplacian that allows to easily generate aligned eigenspaces for multiple registered geometries. We show that the similarity between those aligned high-dimensional spaces provides a locally meaningful score to match shapes. We firstly evaluate the performance of the proposed technique in a point-wise manner focusing on the task of object anomaly localization on the MVTec 3D-AD dataset. Additionally we define a new medical task called automatic Bone Side Estimation (BSE) which we address through a global similarity score derived from coupled eigenspaces. In order to test it we propose a benchmark collecting bone surface structures from various public datasets. Our matching technique based on Coupled Laplacian outperforms other methods by reaching an impressive accuracy on both tasks.

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Overcoming Generic Knowledge Loss with Selective Parameter Update

http://arxiv.org/abs/2308.12462

Wenxuan Zhang, Paul Janson, Rahaf Aljundi, Mohamed Elhoseiny

Foundation models encompass an extensive knowledge base and offer remarkable transferability. However this knowledge becomes outdated or insufficient over time. The challenge lies in continuously updating foundation models to accommodate novel information while retaining their original capabilities. Leveraging the fact that foundation models have initial knowledge on various tasks and domains we propose a novel approach that instead of updating all parameters equally localizes the updates to a sparse set of parameters relevant to the task being learned. We strike a balance between efficiency and new task performance while maintaining the transferability and generalizability of foundation models. We extensively evaluate our method on foundational vision-language models with a diverse spectrum of continual learning tasks. Our method achieves improvements on the accuracy of the newly learned tasks up to 7% while preserving the pretraining knowledge with a negligible decrease of 0.9% on a representative control set accuracy.

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Desigen: A Pipeline for Controllable Design Template Generation

http://arxiv.org/abs/2403.09093

Haohan Weng, Danqing Huang, Yu Qiao, Zheng Hu, Chin-Yew Lin, Tong Zhang, C. L. Philip Chen

Templates serve as a good starting point to implement a design (e.g. banner slide) but it takes great effort from designers to manually create. In this paper we present Desigen an automatic template creation pipeline which generates background images as well as harmonious layout elements over the background. Different from natural images a background image should preserve enough non-salient space for the overlaying layout elements. To equip existing advanced diffusion-based models with stronger spatial control we propose two simple but effective techniques to constrain the saliency distribution and reduce the attention weight in desired regions during the background generation process. Then conditioned on the background we synthesize the layout with a Transformer-based autoregressive generator. To achieve a more harmonious composition we propose an iterative inference strategy to adjust the synthesized background and layout in multiple rounds. We constructed a design dataset with more than 40k advertisement banners to verify our approach. Extensive experiments demonstrate that the proposed pipeline generates high-quality templates comparable to human designers. More than a single-page design we further show an application of presentation generation that outputs a set of theme-consistent slides. The data and code are available at https://whaohan.github.io/desigen.

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Diff-BGM: A Diffusion Model for Video Background Music Generation

Sizhe Li, Yiming Qin, Minghang Zheng, Xin Jin, Yang Liu

When editing a video a piece of attractive background music is indispensable. However video background music generation tasks face several challenges for example the lack of suitable training datasets and the difficulties in flexibly controlling the music generation process and sequentially aligning the video and music. In this work we first propose a high-quality music-video dataset BGM909 with detailed annotation and shot detection to provide multi-modal information about the video and music. We then present evaluation metrics to assess music quality including music diversity and alignment between music and video with retrieval precision metrics. Finally we propose the Diff-BGM framework to automatically generate the background music for a given video which uses different signals to control different aspects of the music during the generation process i.e. uses dynamic video features to control music rhythm and semantic features to control the melody and atmosphere. We propose to align the video and music sequentially by introducing a segment-aware cross-attention layer. Experiments verify the effectiveness of our proposed method. The code and models are available at https://github.com/sizhelee/Diff-BGM.

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Looking Similar Sounding Different: Leveraging Counterfactual Cross-Modal Pairs for Audiovisual Representation Learning

http://arxiv.org/abs/2304.05600

Nikhil Singh, Chih-Wei Wu, Iroro Orife, Mahdi Kalayeh

Audiovisual representation learning typically relies on the correspondence between sight and sound. However there are often multiple audio tracks that can correspond with a visual scene. Consider for example different conversations on the same crowded street. The effect of such counterfactual pairs on audiovisual representation learning has not been previously explored. To investigate this we use dubbed versions of movies and television shows to augment cross-modal contrastive learning. Our approach learns to represent alternate audio tracks differing only in speech similarly to the same video. Our results from a comprehensive set of experiments investigating different training strategies show this general approach improves performance on a range of downstream auditory and audiovisual tasks without majorly affecting linguistic task performance overall. These findings highlight the importance of considering speech variation when learning scene-level audiovisual correspondences and suggest that dubbed audio can be a useful augmentation technique for training audiovisual models toward more robust performance on diverse downstream tasks.

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Multi-criteria Token Fusion with One-step-ahead Attention for Efficient Vision Transformers

http://arxiv.org/abs/2403.10030

Sanghyeok Lee, Joonmyung Choi, Hyunwoo J. Kim

Vision Transformer (ViT) has emerged as a prominent backbone for computer vision. For more efficient ViTs recent works lessen the quadratic cost of the self-attention layer by pruning or fusing the redundant tokens. However these works faced the speed-accuracy trade-off caused by the loss of information. Here we argue that token fusion needs to consider diverse relations between tokens to minimize information loss. In this paper we propose a Multi-criteria Token Fusion (MCTF) that gradually fuses the tokens based on multi-criteria (i.e. similarity informativeness and size of fused tokens). Further we utilize the one-step-ahead attention which is the improved approach to capture the informativeness of the tokens. By training the model equipped with MCTF using a token reduction consistency we achieve the best speed-accuracy trade-off in the image classification (ImageNet1K). Experimental results prove that MCTF consistently surpasses the previous reduction methods with and without training. Specifically DeiT-T and DeiT-S with MCTF reduce FLOPs by about 44% while improving the performance (+0.5% and +0.3%) over the base model respectively. We also demonstrate the applicability of MCTF in various Vision Transformers (e.g. T2T-ViT LV-ViT) achieving at least 31% speedup without performance degradation. Code is available at https://github.com/mlvlab/MCTF.

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Towards HDR and HFR Video from Rolling-Mixed-Bit Spikings

Yakun Chang, Yeliduosi Xiaokaiti, Yujia Liu, Bin Fan, Zhaojun Huang, Tiejun Huang, Boxin Shi

The spiking cameras offer the benefits of high dynamic range (HDR) high temporal resolution and low data redundancy. However reconstructing HDR videos in high-speed conditions using single-bit spikings presents challenges due to the limited bit depth. Increasing the bit depth of the spikings is advantageous for boosting HDR performance but the readout efficiency will be decreased which is unfavorable for achieving a high frame rate (HFR) video. To address these challenges we propose a readout mechanism to obtain rolling-mixed-bit (RMB) spikings which involves interleaving multi-bit spikings within the single-bit spikings in a rolling manner thereby combining the characteristics of high bit depth and efficient readout. Furthermore we introduce RMB-Net for reconstructing HDR and HFR videos. RMB-Net comprises a cross-bit attention block for fusing mixed-bit spikings and a cross-time attention block for achieving temporal fusion. Extensive experiments conducted on synthetic and real-synthetic data demonstrate the superiority of our method. For instance pure 3-bit spikings result in 3 times of data volume whereas our method achieves comparable performance with less than 2% increase in data volume.

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Scaling Up Video Summarization Pretraining with Large Language Models

http://arxiv.org/abs/2404.03398

Dawit Mureja Argaw, Seunghyun Yoon, Fabian Caba Heilbron, Hanieh Deilamsalehy, Trung Bui, Zhaowen Wang, Franck Dernoncourt, Joon Son Chung

Long-form video content constitutes a significant portion of internet traffic making automated video summarization an essential research problem. However existing video summarization datasets are notably limited in their size constraining the effectiveness of state-of-the-art methods for generalization. Our work aims to overcome this limitation by capitalizing on the abundance of long-form videos with dense speech-to-video alignment and the remarkable capabilities of recent large language models (LLMs) in summarizing long text. We introduce an automated and scalable pipeline for generating a large-scale video summarization dataset using LLMs as Oracle summarizers. By leveraging the generated dataset we analyze the limitations of existing approaches and propose a new video summarization model that effectively addresses them. To facilitate further research in the field our work also presents a new benchmark dataset that contains 1200 long videos each with high-quality summaries annotated by professionals. Extensive experiments clearly indicate that our proposed approach sets a new state-of-the-art in video summarization across several benchmarks.

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Continuous Optical Zooming: A Benchmark for Arbitrary-Scale Image Super-Resolution in Real World

Huiyuan Fu, Fei Peng, Xianwei Li, Yejun Li, Xin Wang, Huadong Ma

Most current arbitrary-scale image super-resolution (SR) methods has commonly relied on simulated data generated by simple synthetic degradation models (e.g. bicubic downsampling) at continuous various scales thereby falling short in capturing the complex degradation of real-world images. This limitation hinders the visual quality of these methods when applied to real-world images. To address this issue we propose the Continuous Optical Zooming dataset (COZ) by constructing an automatic imaging system to collect images at fine-grained various focal lengths within a specific range and providing strict image pair alignment. The COZ dataset serves as a benchmark to provide real-world data for training and testing arbitrary-scale SR models. To enhance the model's robustness against real-world image degradation we propose a Local Mix Implicit network (LMI) based on the MLP-mixer architecture and meta-learning which directly learns the local texture information by simultaneously mixing features and coordinates of multiple independent points. The extensive experiments demonstrate the superior performance of the arbitrary-scale SR models trained on the COZ dataset compared to models trained on simulated data. Our LMI model exhibits the superior effectiveness compared to other models. This study is of great significance in developing more efficient algorithms and improving the performance of arbitrary-scale image SR methods in practical applications. Our dataset and codes are available at https://github.com/pf0607/COZ.

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Sharingan: A Transformer Architecture for Multi-Person Gaze Following

Samy Tafasca, Anshul Gupta, Jean-Marc Odobez

Gaze is a powerful form of non-verbal communication that humans develop from an early age. As such modeling this behavior is an important task that can benefit a broad set of application domains ranging from robotics to sociology. In particular the gaze following task in computer vision is defined as the prediction of the 2D pixel coordinates where a person in the image is looking. Previous attempts in this area have primarily centered on CNN-based architectures but they have been constrained by the need to process one person at a time which proves to be highly inefficient. In this paper we introduce a novel and effective multi-person transformer-based architecture for gaze prediction. While there exist prior works using transformers for multi-person gaze prediction they use a fixed set of learnable embeddings to decode both the person and its gaze target which requires a matching step afterward to link the predictions with the annotations. Thus it is difficult to quantitatively evaluate these methods reliably with the available benchmarks or integrate them into a larger human behavior understanding system. Instead we are the first to propose a multi-person transformer-based architecture that maintains the original task formulation and ensures control over the people fed as input. Our main contribution lies in encoding the person-specific information into a single controlled token to be processed alongside image tokens and using its output for prediction based on a novel multiscale decoding mechanism. Our new architecture achieves state-of-the-art results on the GazeFollow VideoAttentionTarget and ChildPlay datasets and outperforms comparable multi-person architectures with a notable margin. Our code checkpoints and data extractions will be made publicly available soon.

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ViewFusion: Towards Multi-View Consistency via Interpolated Denoising

http://arxiv.org/abs/2402.18842

Xianghui Yang, Yan Zuo, Sameera Ramasinghe, Loris Bazzani, Gil Avraham, Anton van den Hengel

Novel-view synthesis through diffusion models has demonstrated remarkable potential for generating diverse and high-quality images. Yet the independent process of image generation in these prevailing methods leads to challenges in maintaining multiple-view consistency. To address this we introduce ViewFusion a novel training-free algorithm that can be seamlessly integrated into existing pre-trained diffusion models. Our approach adopts an auto-regressive method that implicitly leverages previously generated views as context for the next view generation ensuring robust multi-view consistency during the novel-view generation process. Through a diffusion process that fuses known-view information via interpolated denoising our framework successfully extends single-view conditioned models to work in multiple-view conditional settings without any additional fine-tuning. Extensive experimental results demonstrate the effectiveness of ViewFusion in generating consistent and detailed novel views.

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SketchINR: A First Look into Sketches as Implicit Neural Representations

http://arxiv.org/abs/2403.09344

Hmrishav Bandyopadhyay, Ayan Kumar Bhunia, Pinaki Nath Chowdhury, Aneeshan Sain, Tao Xiang, Timothy Hospedales, Yi-Zhe Song

We propose SketchINR to advance the representation of vector sketches with implicit neural models. A variable length vector sketch is compressed into a latent space of fixed dimension that implicitly encodes the underlying shape as a function of time and strokes. The learned function predicts the xy point coordinates in a sketch at each time and stroke. Despite its simplicity SketchINR outperforms existing representations at multiple tasks: (i) Encoding an entire sketch dataset into a fixed size latent vector SketchINR gives 60x and 10x data compression over raster and vector sketches respectively. (ii) SketchINR's auto-decoder provides a much higher-fidelity representation than other learned vector sketch representations and is uniquely able to scale to complex vector sketches such as FS-COCO. (iii) SketchINR supports parallelisation that can decode/render 100x faster than other learned vector representations such as SketchRNN. (iv) SketchINR for the first time emulates the human ability to reproduce a sketch with varying abstraction in terms of number and complexity of strokes. As a first look at implicit sketches SketchINR's compact high-fidelity representation will support future work in modelling long and complex sketches.

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Open-Vocabulary Segmentation with Semantic-Assisted Calibration

http://arxiv.org/abs/2312.04089

Yong Liu, Sule Bai, Guanbin Li, Yitong Wang, Yansong Tang

This paper studies open-vocabulary segmentation (OVS) through calibrating in-vocabulary and domain-biased embedding space with generalized contextual prior of CLIP. As the core of open-vocabulary understanding alignment of visual content with the semantics of unbounded text has become the bottleneck of this field. To address this challenge recent works propose to utilize CLIP as an additional classifier and aggregate model predictions with CLIP classification results. Despite their remarkable progress performance of OVS methods in relevant scenarios is still unsatisfactory compared with supervised counterparts. We attribute this to the in-vocabulary embedding and domain-biased CLIP prediction. To this end we present a Semantic-assisted CAlibration Network (SCAN). In SCAN we incorporate generalized semantic prior of CLIP into proposal embedding to avoid collapsing on known categories. Besides a contextual shift strategy is applied to mitigate the lack of global context and unnatural background noise. With above designs SCAN achieves state-of-the-art performance on all popular open-vocabulary segmentation benchmarks. Furthermore we also focus on the problem of existing evaluation system that ignores semantic duplication across categories and propose a new metric called Semantic-Guided IoU (SG-IoU).

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MatchU: Matching Unseen Objects for 6D Pose Estimation from RGB-D Images

Junwen Huang, Hao Yu, Kuan-Ting Yu, Nassir Navab, Slobodan Ilic, Benjamin Busam

Recent learning methods for object pose estimation require resource-intensive training for each individual object instance or category hampering their scalability in real applications when confronted with previously unseen objects. In this paper we propose MatchU a Fuse-Describe-Match strategy for 6D pose estimation from RGB-D images. MatchU is a generic approach that fuses 2D texture and 3D geometric cues for 6D pose prediction of unseen objects. We rely on learning geometric 3D descriptors that are rotation-invariant by design. By encoding pose-agnostic geometry the learned descriptors naturally generalize to unseen objects and capture symmetries. To tackle ambiguous associations using 3D geometry only we fuse additional RGB information into our descriptor. This is achieved through a novel attention-based mechanism that fuses cross-modal information together with a matching loss that leverages the latent space learned from RGB data to guide the descriptor learning process. Extensive experiments reveal the generalizability of both the RGB-D fusion strategy as well as the descriptor efficacy. Benefiting from the novel designs MatchU surpasses all existing methods by a significant margin in terms of both accuracy and speed even without the requirement of expensive re-training or rendering.

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Towards a Perceptual Evaluation Framework for Lighting Estimation

http://arxiv.org/abs/2312.04334

Justine Giroux, Mohammad Reza Karimi Dastjerdi, Yannick Hold-Geoffroy, Javier Vazquez-Corral, Jean-François Lalonde

Progress in lighting estimation is tracked by computing existing image quality assessment (IQA) metrics on images from standard datasets. While this may appear to be a reasonable approach we demonstrate that doing so does not correlate to human preference when the estimated lighting is used to relight a virtual scene into a real photograph. To study this we design a controlled psychophysical experiment where human observers must choose their preference amongst rendered scenes lit using a set of lighting estimation algorithms selected from the recent literature and use it to analyse how these algorithms perform according to human perception. Then we demonstrate that none of the most popular IQA metrics from the literature taken individually correctly represent human perception. Finally we show that by learning a combination of existing IQA metrics we can more accurately represent human preference. This provides a new perceptual framework to help evaluate future lighting estimation algorithms. To encourage future research all (anonymised) perceptual data and code are available at https://lvsn.github.io/PerceptionMetric/.

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Bridging the Synthetic-to-Authentic Gap: Distortion-Guided Unsupervised Domain Adaptation for Blind Image Quality Assessment

http://arxiv.org/abs/2405.04167

Aobo Li, Jinjian Wu, Yongxu Liu, Leida Li

The annotation of blind image quality assessment (BIQA) is labor-intensive and time-consuming especially for authentic images. Training on synthetic data is expected to be beneficial but synthetically trained models often suffer from poor generalization in real domains due to domain gaps. In this work we make a key observation that introducing more distortion types in the synthetic dataset may not improve or even be harmful to generalizing authentic image quality assessment. To solve this challenge we propose distortion-guided unsupervised domain adaptation for BIQA (DGQA) a novel framework that leverages adaptive multi-domain selection via prior knowledge from distortion to match the data distribution between the source domains and the target domain thereby reducing negative transfer from the outlier source domains. Extensive experiments on two cross-domain settings (synthetic distortion to authentic distortion and synthetic distortion to algorithmic distortion) have demonstrated the effectiveness of our proposed DGQA. Besides DGQA is orthogonal to existing model-based BIQA methods and can be used in combination with such models to improve performance with less training data.

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Coherent Temporal Synthesis for Incremental Action Segmentation

http://arxiv.org/abs/2403.06102

Guodong Ding, Hans Golong, Angela Yao

Data replay is a successful incremental learning technique for images. It prevents catastrophic forgetting by keeping a reservoir of previous data original or synthesized to ensure the model retains past knowledge while adapting to novel concepts. However its application in the video domain is rudimentary as it simply stores frame exemplars for action recognition. This paper presents the first exploration of video data replay techniques for incremental action segmentation focusing on action temporal modeling. We propose a Temporally Coherent Action (TCA) model which represents actions using a generative model instead of storing individual frames. The integration of a conditioning variable that captures temporal coherence allows our model to understand the evolution of action features over time. Therefore action segments generated by TCA for replay are diverse and temporally coherent. In a 10-task incremental setup on the Breakfast dataset our approach achieves significant increases in accuracy for up to 22% compared to the baselines.

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HiFi4G: High-Fidelity Human Performance Rendering via Compact Gaussian Splatting

http://arxiv.org/abs/2312.03461

Yuheng Jiang, Zhehao Shen, Penghao Wang, Zhuo Su, Yu Hong, Yingliang Zhang, Jingyi Yu, Lan Xu

We have recently seen tremendous progress in photo-real human modeling and rendering. Yet efficiently rendering realistic human performance and integrating it into the rasterization pipeline remains challenging. In this paper we present HiFi4G an explicit and compact Gaussian-based approach for high-fidelity human performance rendering from dense footage. Our core intuition is to marry the 3D Gaussian representation with non-rigid tracking achieving a compact and compression-friendly representation. We first propose a dual-graph mechanism to obtain motion priors with a coarse deformation graph for effective initialization and a fine-grained Gaussian graph to enforce subsequent constraints. Then we utilize a 4D Gaussian optimization scheme with adaptive spatial-temporal regularizers to effectively balance the non-rigid prior and Gaussian updating. We also present a companion compression scheme with residual compensation for immersive experiences on various platforms. It achieves a substantial compression rate of approximately 25 times with less than 2MB of storage per frame. Extensive experiments demonstrate the effectiveness of our approach which significantly outperforms existing approaches in terms of optimization speed rendering quality and storage overhead.

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G-FARS: Gradient-Field-based Auto-Regressive Sampling for 3D Part Grouping

Junfeng Cheng, Tania Stathaki

This paper proposes a novel task named "3D part grouping". Suppose there is a mixed set containing scattered parts from various shapes. This task requires algorithms to find out every possible combination among all the parts. To address this challenge we propose the so called Gradient Field-based Auto-Regressive Sampling framework (G-FARS) tailored specifically for the 3D part grouping task. In our framework we design a gradient-field-based selection graph neural network (GNN) to learn the gradients of a log conditional probability density in terms of part selection where the condition is the given mixed part set. This innovative approach implemented through the gradient-field-based selection GNN effectively captures complex relationships among all the parts in the input. Upon completion of the training process our framework becomes capable of autonomously grouping 3D parts by iteratively selecting them from the mixed part set leveraging the knowledge acquired by the trained gradient-field-based selection GNN. Our code is available at: https://github.com/J-F-Cheng/G-FARS-3DPartGrouping.

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Towards High-fidelity Artistic Image Vectorization via Texture-Encapsulated Shape Parameterization

Ye Chen, Bingbing Ni, Jinfan Liu, Xiaoyang Huang, Xuanhong Chen

We develop a novel vectorized image representation scheme accommodating both shape/geometry and texture in a decoupled way particularly tailored for reconstruction and editing tasks of artistic/design images such as Emojis and Cliparts. In the heart of this representation is a set of sparsely and unevenly located 2D control points. On one hand these points constitute a collection of parametric/vectorized geometric primitives (e.g. curves and closed shapes) describing the shape characteristics of the target image. On the other hand local texture codes in terms of implicit neural network parameters are spatially distributed into each control point yielding local coordinate-to-RGB mappings within the anchored region of each control point. In the meantime a zero-shot learning algorithm is developed to decompose an arbitrary raster image into the above representation for the sake of high-fidelity image vectorization with convenient editing ability. Extensive experiments on a series of image vectorization and editing tasks well demonstrate the high accuracy offered by our proposed method with a significantly higher image compression ratio over prior art.

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On Exact Inversion of DPM-Solvers

http://arxiv.org/abs/2311.18387

Seongmin Hong, Kyeonghyun Lee, Suh Yoon Jeon, Hyewon Bae, Se Young Chun

Diffusion probabilistic models (DPMs) are a key component in modern generative models. DPM-solvers have achieved reduced latency and enhanced quality significantly but have posed challenges to find the exact inverse (i.e. finding the initial noise from the given image). Here we investigate the exact inversions for DPM-solvers and propose algorithms to perform them when samples are generated by the first-order as well as higher-order DPM-solvers. For each explicit denoising step in DPM-solvers we formulated the inversions using implicit methods such as gradient descent or forward step method to ensure the robustness to large classifier-free guidance unlike the prior approach using fixed-point iteration. Experimental results demonstrated that our proposed exact inversion methods significantly reduced the error of both image and noise reconstructions greatly enhanced the ability to distinguish invisible watermarks and well prevented unintended background changes consistently during image editing.

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EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything

http://arxiv.org/abs/2312.00863

Yunyang Xiong, Bala Varadarajan, Lemeng Wu, Xiaoyu Xiang, Fanyi Xiao, Chenchen Zhu, Xiaoliang Dai, Dilin Wang, Fei Sun, Forrest Iandola, Raghuraman Krishnamoorthi, Vikas Chandra

Segment Anything Model (SAM) has emerged as a powerful tool for numerous vision applications. A key component that drives the impressive performance for zero-shot transfer and high versatility is a super large Transformer model trained on the extensive high-quality SA-1B dataset. While beneficial the huge computation cost of SAM model has limited its applications to wider real-world applications. To address this limitation we propose EfficientSAMs light-weight SAM models that exhibits decent performance with largely reduced complexity. Our idea is based on leveraging masked image pretraining SAMI which learns to reconstruct features from SAM image encoder for effective visual representation learning. Further we take SAMI-pretrained light-weight image encoders and mask decoder to build EfficientSAMs and finetune the models on SA-1B for segment anything task. We perform evaluations on multiple vision tasks including image classification object detection instance segmentation and semantic segmentation and find that our proposed pretraining method SAMI consistently outperforms other masked image pretraining methods. On segment anything task such as zero-shot instance segmentation our EfficientSAMs with SAMI-pretrained lightweight image encoders perform favorably with a significant gain (e.g. 4 AP on COCO/LVIS) over other fast SAM models. Our EfficientSAM code and models are available at https://github.com/yformer/EfficientSAM.

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ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous Vehicles

http://arxiv.org/abs/2405.14062

Jiawei Zhang, Chejian Xu, Bo Li

We present ChatScene a Large Language Model (LLM)-based agent that leverages the capabilities of LLMs to generate safety-critical scenarios for autonomous vehicles. Given unstructured language instructions the agent first generates textually described traffic scenarios using LLMs. These scenario descriptions are subsequently broken down into several sub-descriptions for specified details such as behaviors and locations of vehicles. The agent then distinctively transforms the textually described sub-scenarios into domain-specific languages which then generate actual code for prediction and control in simulators facilitating the creation of diverse and complex scenarios within the CARLA simulation environment. A key part of our agent is a comprehensive knowledge retrieval component which efficiently translates specific textual descriptions into corresponding domain-specific code snippets by training a knowledge database containing the scenario description and code pairs. Extensive experimental results underscore the efficacy of ChatScene in improving the safety of autonomous vehicles. For instance the scenarios generated by ChatScene show a 15% increase in collision rates compared to state-of-the-art baselines when tested against different reinforcement learning-based ego vehicles. Furthermore we show that by using our generated safety-critical scenarios to fine-tune different RL-based autonomous driving models they can achieve a 9% reduction in collision rates surpassing current SOTA methods. ChatScene effectively bridges the gap between textual descriptions of traffic scenarios and practical CARLA simulations providing a unified way to conveniently generate safety-critical scenarios for safety testing and improvement for AVs.

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CAMEL: CAusal Motion Enhancement Tailored for Lifting Text-driven Video Editing

Guiwei Zhang, Tianyu Zhang, Guanglin Niu, Zichang Tan, Yalong Bai, Qing Yang

Text-driven video editing poses significant challenges in exhibiting flicker-free visual continuity while preserving the inherent motion patterns of original videos. Existing methods operate under a paradigm where motion and appearance are intricately intertwined. This coupling leads to the network either over-fitting appearance content -- failing to capture motion patterns -- or focusing on motion patterns at the expense of content generalization to diverse textual scenarios. Inspired by the pivotal role of wavelet transform in dissecting video sequences we propose CAusal Motion Enhancement tailored for Lifting text-driven video editing (CAMEL) a novel technique with two core designs. First we introduce motion prompts designed to summarize motion concepts from video templates through direct optimization. The optimized prompts are purposefully integrated into latent representations of diffusion models to enhance the motion fidelity of generated results. Second to enhance motion coherence and extend the generalization of appearance content to creative textual prompts we propose the causal motion-enhanced attention mechanism. This mechanism is implemented in tandem with a novel causal motion filter synergistically enhancing the motion coherence of disentangled high-frequency components and concurrently preserving the generalization of appearance content across various textual scenarios. Extensive experimental results show the superior performance of CAMEL.

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Teeth-SEG: An Efficient Instance Segmentation Framework for Orthodontic Treatment based on Multi-Scale Aggregation and Anthropic Prior Knowledge

Bo Zou, Shaofeng Wang, Hao Liu, Gaoyue Sun, Yajie Wang, FeiFei Zuo, Chengbin Quan, Youjian Zhao

Teeth localization segmentation and labeling in 2D images have great potential in modern dentistry to enhance dental diagnostics treatment planning and population-based studies on oral health. However general instance segmentation frameworks are incompetent due to 1) the subtle differences between some teeth' shapes (e.g. maxillary first premolar and second premolar) 2) the teeth's position and shape variation across subjects and 3) the presence of abnormalities in the dentition (e.g. caries and edentulism). To address these problems we propose a ViT-based framework named TeethSEG which consists of stacked Multi-Scale Aggregation (MSA) blocks and an Anthropic Prior Knowledge (APK) layer. Specifically to compose the two modules we design 1) a unique permutation-based upscaler to ensure high efficiency while establishing clear segmentation boundaries with 2) multi-head self/cross-gating layers to emphasize particular semantics meanwhile maintaining the divergence between token embeddings. Besides we collect 3) the first open-sourced intraoral image dataset IO150K which comprises over 150k intraoral photos and all photos are annotated by orthodontists using a human-machine hybrid algorithm. Experiments on IO150K demonstrate that our TeethSEG outperforms the state-of-the-art segmentation models on dental image segmentation.

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FocSAM: Delving Deeply into Focused Objects in Segmenting Anything

http://arxiv.org/abs/2405.18706

You Huang, Zongyu Lan, Liujuan Cao, Xianming Lin, Shengchuan Zhang, Guannan Jiang, Rongrong Ji

The Segment Anything Model (SAM) marks a notable milestone in segmentation models highlighted by its robust zero-shot capabilities and ability to handle diverse prompts. SAM follows a pipeline that separates interactive segmentation into image preprocessing through a large encoder and interactive inference via a lightweight decoder ensuring efficient real-time performance. However SAM faces stability issues in challenging samples upon this pipeline. These issues arise from two main factors. Firstly the image preprocessing disables SAM to dynamically use image-level zoom-in strategies to refocus on the target object during interaction. Secondly the lightweight decoder struggles to sufficiently integrate interactive information with image embeddings. To address these two limitations we propose FocSAM with a pipeline redesigned on two pivotal aspects. First we propose Dynamic Window Multi-head Self-Attention (Dwin-MSA) to dynamically refocus SAM's image embeddings on the target object. Dwin-MSA localizes attention computations around the target object enhancing object-related embeddings with minimal computational overhead. Second we propose Pixel-wise Dynamic ReLU (P-DyReLU) to enable sufficient integration of interactive information from a few initial clicks that have significant impacts on the overall segmentation results. Experimentally FocSAM augments SAM's interactive segmentation performance to match the existing state-of-the-art method in segmentation quality requiring only about 5.6% of this method's inference time on CPUs. Code is available at https://github.com/YouHuang67/focsam.

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DMR: Decomposed Multi-Modality Representations for Frames and Events Fusion in Visual Reinforcement Learning

Haoran Xu, Peixi Peng, Guang Tan, Yuan Li, Xinhai Xu, Yonghong Tian

We explore visual reinforcement learning (RL) using two complementary visual modalities: frame-based RGB camera and event-based Dynamic Vision Sensor (DVS). Existing multi-modality visual RL methods often encounter challenges in effectively extracting task-relevant information from multiple modalities while suppressing the increased noise only using indirect reward signals instead of pixel-level supervision. To tackle this we propose a Decomposed Multi-Modality Representation (DMR) framework for visual RL. It explicitly decomposes the inputs into three distinct components: combined task-relevant features (co-features) RGB-specific noise and DVS-specific noise. The co-features represent the full information from both modalities that is relevant to the RL task; the two noise components each constrained by a data reconstruction loss to avoid information leak are contrasted with the co-features to maximize their difference. Extensive experiments demonstrate that by explicitly separating the different types of information our approach achieves substantially improved policy performance compared to state-of-the-art approaches.

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DiffuseMix: Label-Preserving Data Augmentation with Diffusion Models

http://arxiv.org/abs/2405.14881

Khawar Islam, Muhammad Zaigham Zaheer, Arif Mahmood, Karthik Nandakumar

Recently a number of image-mixing-based augmentation techniques have been introduced to improve the generalization of deep neural networks. In these techniques two or more randomly selected natural images are mixed together to generate an augmented image. Such methods may not only omit important portions of the input images but also introduce label ambiguities by mixing images across labels resulting in misleading supervisory signals. To address these limitations we propose DIFFUSEMIX a novel data augmentation technique that leverages a diffusion model to reshape training images supervised by our bespoke conditional prompts. First concatenation of a partial natural image and its generated counterpart is obtained which helps in avoiding the generation of unrealistic images or label ambiguities. Then to enhance resilience against adversarial attacks and improves safety measures a randomly selected structural pattern from a set of fractal images is blended into the concatenated image to form the final augmented image for training. Our empirical results on seven different datasets reveal that DIFFUSEMIX achieves superior performance compared to existing state- of-the-art methods on tasks including general classification fine-grained classification fine-tuning data scarcity and adversarial robustness.

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PRDP: Proximal Reward Difference Prediction for Large-Scale Reward Finetuning of Diffusion Models

http://arxiv.org/abs/2402.08714

Fei Deng, Qifei Wang, Wei Wei, Tingbo Hou, Matthias Grundmann

Reward finetuning has emerged as a promising approach to aligning foundation models with downstream objectives. Remarkable success has been achieved in the language domain by using reinforcement learning (RL) to maximize rewards that reflect human preference. However in the vision domain existing RL-based reward finetuning methods are limited by their instability in large-scale training rendering them incapable of generalizing to complex unseen prompts. In this paper we propose Proximal Reward Difference Prediction (PRDP) enabling stable black-box reward finetuning for diffusion models for the first time on large-scale prompt datasets with over 100K prompts. Our key innovation is the Reward Difference Prediction (RDP) objective that has the same optimal solution as the RL objective while enjoying better training stability. Specifically the RDP objective is a supervised regression objective that tasks the diffusion model with predicting the reward difference of generated image pairs from their denoising trajectories. We theoretically prove that the diffusion model that obtains perfect reward difference prediction is exactly the maximizer of the RL objective. We further develop an online algorithm with proximal updates to stably optimize the RDP objective. In experiments we demonstrate that PRDP can match the reward maximization ability of well-established RL-based methods in small-scale training. Furthermore through large-scale training on text prompts from the Human Preference Dataset v2 and the Pick-a-Pic v1 dataset PRDP achieves superior generation quality on a diverse set of complex unseen prompts whereas RL-based methods completely fail.

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FREE: Faster and Better Data-Free Meta-Learning

http://arxiv.org/abs/2405.00984

Yongxian Wei, Zixuan Hu, Zhenyi Wang, Li Shen, Chun Yuan, Dacheng Tao

Data-Free Meta-Learning (DFML) aims to extract knowledge from a collection of pre-trained models without requiring the original data presenting practical benefits in contexts constrained by data privacy concerns. Current DFML methods primarily focus on the data recovery from these pre-trained models. However they suffer from slow recovery speed and overlook gaps inherent in heterogeneous pre-trained models. In response to these challenges we introduce the Faster and Better Data-Free Meta-Learning (FREE) framework which contains: (i) a meta-generator for rapidly recovering training tasks from pre-trained models; and (ii) a meta-learner for generalizing to new unseen tasks. Specifically within the module Faster Inversion via Meta-Generator each pre-trained model is perceived as a distinct task. The meta-generator can rapidly adapt to a specific task in just five steps significantly accelerating the data recovery. Furthermore we propose Better Generalization via Meta-Learner and introduce an implicit gradient alignment algorithm to optimize the meta-learner. This is achieved as aligned gradient directions alleviate potential conflicts among tasks from heterogeneous pre-trained models. Empirical experiments on multiple benchmarks affirm the superiority of our approach marking a notable speed-up (20x) and performance enhancement (1.42% 4.78%) in comparison to the state-of-the-art.

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Bayesian Diffusion Models for 3D Shape Reconstruction

http://arxiv.org/abs/2403.06973

Haiyang Xu, Yu Lei, Zeyuan Chen, Xiang Zhang, Yue Zhao, Yilin Wang, Zhuowen Tu

We present Bayesian Diffusion Models (BDM) a prediction algorithm that performs effective Bayesian inference by tightly coupling the top-down (prior) information with the bottom-up (data-driven) procedure via joint diffusion processes. We demonstrate the application of BDM on the 3D shape reconstruction task. Compared to standard deep learning data-driven approaches relying on supervised data our BDM can bring in rich prior information trained in an unsupervised manner to improve the bottom-up 3D reconstruction. As opposed to the traditional Bayesian frameworks where explicitly learned prior and data-driven distributions are required for gradient computation and combination BDM performs a seamless fusion of the two via coupled diffusion processes with learned gradient computation networks. The specialty of our Bayesian Diffusion Models (BDM) lies in its capability to engage the active and effective information exchange and fusion of the top-down and bottom-up processes where each itself is a diffusion process. We demonstrate state-of-the-art results on both synthetic and real-world benchmarks for 3D shape reconstruction. Project link: https://mlpc-ucsd.github.io/BDM

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Task-Customized Mixture of Adapters for General Image Fusion

http://arxiv.org/abs/2403.12494

Pengfei Zhu, Yang Sun, Bing Cao, Qinghua Hu

General image fusion aims at integrating important information from multi-source images. However due to the significant cross-task gap the respective fusion mechanism varies considerably in practice resulting in limited performance across subtasks. To handle this problem we propose a novel task-customized mixture of adapters (TC-MoA) for general image fusion adaptively prompting various fusion tasks in a unified model. We borrow the insight from the mixture of experts (MoE) taking the experts as efficient tuning adapters to prompt a pre-trained foundation model. These adapters are shared across different tasks and constrained by mutual information regularization ensuring compatibility with different tasks while complementarity for multi-source images. The task-specific routing networks customize these adapters to extract task-specific information from different sources with dynamic dominant intensity performing adaptive visual feature prompt fusion. Notably our TC-MoA controls the dominant intensity bias for different fusion tasks successfully unifying multiple fusion tasks in a single model. Extensive experiments show that TC-MoA outperforms the competing approaches in learning commonalities while retaining compatibility for general image fusion (multi-modal multi-exposure and multi-focus) and also demonstrating striking controllability on more generalization experiments. The code is available at https://github.com/YangSun22/TC-MoA.

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Bi-SSC: Geometric-Semantic Bidirectional Fusion for Camera-based 3D Semantic Scene Completion

Yujie Xue, Ruihui Li, Fan Wu, Zhuo Tang, Kenli Li, Mingxing Duan

Camera-based Semantic Scene Completion (SSC) is to infer the full geometry of objects and scenes from only 2D images. The task is particularly challenging for those invisible areas due to the inherent occlusions and lighting ambiguity. Existing works ignore the information missing or ambiguous in those shaded and occluded areas resulting in distorted geometric prediction. To address this issue we propose a novel method Bi-SSC bidirectional geometric semantic fusion for camera-based 3D semantic scene completion. The key insight is to use the neighboring structure of objects in the image and the spatial differences from different perspectives to compensate for the lack of information in occluded areas. Specifically we introduce a spatial sensory fusion module with multiple association attention to improve semantic correlation in geometric distributions. This module works within single view and across stereo views to achieve global spatial consistency. Experimental results demonstrate that Bi-SSC outperforms state-of-the-art camera-based methods on SemanticKITTI particularly excelling in those invisible and shaded areas.

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CrossKD: Cross-Head Knowledge Distillation for Object Detection

http://arxiv.org/abs/2306.11369

Jiabao Wang, Yuming Chen, Zhaohui Zheng, Xiang Li, Ming-Ming Cheng, Qibin Hou

Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. In this paper we present a general and effective prediction mimicking distillation scheme called CrossKD which delivers the intermediate features of the student's detection head to the teacher's detection head. The resulting cross-head predictions are then forced to mimic the teacher's predictions. This manner relieves the student's head from receiving contradictory supervision signals from the annotations and the teacher's predictions greatly improving the student's detection performance. Moreover as mimicking the teacher's predictions is the target of KD CrossKD offers more task-oriented information in contrast with feature imitation. On MS COCO with only prediction mimicking losses applied our CrossKD boosts the average precision of GFL ResNet-50 with 1x training schedule from 40.2 to 43.7 outperforming all existing KD methods. In addition our method also works well when distilling detectors with heterogeneous backbones.

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Bi-level Learning of Task-Specific Decoders for Joint Registration and One-Shot Medical Image Segmentation

Xin Fan, Xiaolin Wang, Jiaxin Gao, Jia Wang, Zhongxuan Luo, Risheng Liu

One-shot medical image segmentation (MIS) aims to cope with the expensive time-consuming and inherent human bias annotations. One prevalent method to address one-shot MIS is joint registration and segmentation (JRS) with a shared encoder which mainly explores the voxel-wise correspondence between the labeled data and unlabeled data for better segmentation. However this method omits underlying connections between task-specific decoders for segmentation and registration leading to unstable training. In this paper we propose a novel Bi-level Learning of Task-Specific Decoders for one-shot MIS employing a pretrained fixed shared encoder that is proved to be more quickly adapted to brand-new datasets than existing JRS without fixed shared encoder paradigm. To be more specific we introduce a bi-level optimization training strategy considering registration as a major objective and segmentation as a learnable constraint by leveraging inter-task coupling dependencies. Furthermore we design an appearance conformity constraint strategy that learns the backward transformations generating the fake labeled data used to perform data augmentation instead of the labeled image to avoid performance degradation caused by inconsistent styles between unlabeled data and labeled data in previous methods. Extensive experiments on the brain MRI task across ABIDE ADNI and PPMI datasets demonstrate that the proposed Bi-JROS outperforms state-of-the-art one-shot MIS methods for both segmentation and registration tasks. The code will be available at https://github.com/Coradlut/Bi-JROS.

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Parameter Efficient Self-Supervised Geospatial Domain Adaptation

Linus Scheibenreif, Michael Mommert, Damian Borth

As large-scale foundation models become publicly available for different domains efficiently adapting them to individual downstream applications and additional data modalities has turned into a central challenge. For example foundation models for geospatial and satellite remote sensing applications are commonly trained on large optical RGB or multi-spectral datasets although data from a wide variety of heterogeneous sensors are available in the remote sensing domain. This leads to significant discrepancies between pre-training and downstream target data distributions for many important applications. Fine-tuning large foundation models to bridge that gap incurs high computational cost and can be infeasible when target datasets are small. In this paper we address the question of how large pre-trained foundational transformer models can be efficiently adapted to downstream remote sensing tasks involving different data modalities or limited dataset size. We present a self-supervised adaptation method that boosts downstream linear evaluation accuracy of different foundation models by 4-6% (absolute) across 8 remote sensing datasets while outperforming full fine-tuning when training only 1-2% of the model parameters. Our method significantly improves label efficiency and increases few-shot accuracy by 6-10% on different datasets.

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Defense without Forgetting: Continual Adversarial Defense with Anisotropic & Isotropic Pseudo Replay

http://arxiv.org/abs/2404.01828

Yuhang Zhou, Zhongyun Hua

Deep neural networks have demonstrated susceptibility to adversarial attacks. Adversarial defense techniques often focus on one-shot setting to maintain robustness against attack. However new attacks can emerge in sequences in real-world deployment scenarios. As a result it is crucial for a defense model to constantly adapt to new attacks but the adaptation process can lead to catastrophic forgetting of previously defended against attacks. In this paper we discuss for the first time the concept of continual adversarial defense under a sequence of attacks and propose a lifelong defense baseline called Anisotropic & Isotropic Replay (AIR) which offers three advantages: (1) Isotropic replay ensures model consistency in the neighborhood distribution of new data indirectly aligning the output preference between old and new tasks. (2) Anisotropic replay enables the model to learn a compromise data manifold with fresh mixed semantics for further replay constraints and potential future attacks. (3) A straightforward regularizer mitigates the 'plasticity-stability' trade-off by aligning model output between new and old tasks. Experiment results demonstrate that AIR can approximate or even exceed the empirical performance upper bounds achieved by Joint Training.

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EscherNet: A Generative Model for Scalable View Synthesis

http://arxiv.org/abs/2402.03908

Xin Kong, Shikun Liu, Xiaoyang Lyu, Marwan Taher, Xiaojuan Qi, Andrew J. Davison

We introduce EscherNet a multi-view conditioned diffusion model for view synthesis. EscherNet learns implicit and generative 3D representations coupled with a specialised camera positional encoding allowing precise and continuous relative control of the camera transformation between an arbitrary number of reference and target views. EscherNet offers exceptional generality flexibility and scalability in view synthesis --- it can generate more than 100 consistent target views simultaneously on a single consumer-grade GPU despite being trained with a fixed number of 3 reference views to 3 target views. As a result EscherNet not only addresses zero-shot novel view synthesis but also naturally unifies single- and multi-image 3D reconstruction combining these diverse tasks into a single cohesive framework. Our extensive experiments demonstrate that EscherNet achieves state-of-the-art performance in multiple benchmarks even when compared to methods specifically tailored for each individual problem. This remarkable versatility opens up new directions for designing scalable neural architectures for 3D vision. Project page: https://kxhit.github.io/EscherNet.

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MeaCap: Memory-Augmented Zero-shot Image Captioning

http://arxiv.org/abs/2403.03715

Zequn Zeng, Yan Xie, Hao Zhang, Chiyu Chen, Bo Chen, Zhengjue Wang

Zero-shot image captioning (IC) without well-paired image-text data can be categorized into two main types: training-free and text-only-training methods. While both types integrate pre-trained vision-language models such as CLIP for image-text similarity evaluation and a pre-trained language model (LM) for caption generation their distinction lies in the utilization of textual corpus for LM training. Despite achieving promising performance on certain metrics existing methods commonly suffer from drawbacks. Training-free methods often generate hallucinations whereas text-only-training methods may lack generalization capability. To address these challenges we propose a novel Memory-Augmented zero-shot image Captioning framework (MeaCap). This framework equipped with a textual memory incorporates a retrieve-then-filter module to extract key concepts highly relevant to the image. By leveraging our proposed memory-augmented visual-related fusion score within a keywords-to-sentence LM MeaCap generates concept-centered captions that exhibit high consistency with the image with reduced hallucinations and enriched world knowledge. MeaCap achieves state-of-the-art performance across various zero-shot IC settings. Our code is publicly available at https://github.com/joeyz0z/MeaCap.

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Artist-Friendly Relightable and Animatable Neural Heads

http://arxiv.org/abs/2312.03420

Yingyan Xu, Prashanth Chandran, Sebastian Weiss, Markus Gross, Gaspard Zoss, Derek Bradley

An increasingly common approach for creating photo-realistic digital avatars is through the use of volumetric neural fields. The original neural radiance field (NeRF) allowed for impressive novel view synthesis of static heads when trained on a set of multi-view images and follow up methods showed that these neural representations can be extended to dynamic avatars. Recently new variants also surpassed the usual drawback of baked-in illumination in neural representations showing that static neural avatars can be relit in any environment. In this work we simultaneously tackle both the motion and illumination problem proposing a new method for relightable and animatable neural heads. Our method builds on a proven dynamic avatar approach based on a mixture of volumetric primitives combined with a recently-proposed lightweight hardware setup for relightable neural fields and includes a novel architecture that allows relighting dynamic neural avatars performing unseen expressions in any environment even with nearfield illumination and viewpoints.

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Elite360D: Towards Efficient 360 Depth Estimation via Semantic- and Distance-Aware Bi-Projection Fusion

Hao Ai, Lin Wang

360 depth estimation has recently received great attention for 3D reconstruction owing to its omnidirectional field of view (FoV). Recent approaches are predominantly focused on cross-projection fusion with geometry-based re-projection: they fuse 360 images with equirectangular projection (ERP) and another projection type e.g. cubemap projection to estimate depth with the ERP format. However these methods suffer from 1) limited local receptive fields making it hardly possible to capture large FoV scenes and 2) prohibitive computational cost caused by the complex cross-projection fusion module design. In this paper we propose Elite360D a novel framework that inputs the ERP image and icosahedron projection (ICOSAP) point set which is undistorted and spatially continuous. Elite360D is superior in its capacity in learning a representation from a local-with-global perspective. With a flexible ERP image encoder it includes an ICOSAP point encoder and a Bi-projection Bi-attention Fusion (B2F) module (totally 1M parameters). Specifically the ERP image encoder can take various perspective image-trained backbones (e.g. ResNet Transformer) to extract local features. The point encoder extracts the global features from the ICOSAP. Then the B2F module captures the semantic- and distance-aware dependencies between each pixel of the ERP feature and the entire ICOSAP feature set. Without specific backbone design and obvious computational cost increase Elite360D outperforms the prior arts on several benchmark datasets.

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From Feature to Gaze: A Generalizable Replacement of Linear Layer for Gaze Estimation

Yiwei Bao, Feng Lu

Deep-learning-based gaze estimation approaches often suffer from notable performance degradation in unseen target domains. One of the primary reasons is that the Fully Connected layer is highly prone to overfitting when mapping the high-dimensional image feature to 3D gaze. In this paper we propose Analytical Gaze Generalization framework (AGG) to improve the generalization ability of gaze estimation models without touching target domain data. The AGG consists of two modules the Geodesic Projection Module (GPM) and the Sphere-Oriented Training (SOT). GPM is a generalizable replacement of FC layer which projects high-dimensional image features to 3D space analytically to extract the principle components of gaze. Then we propose Sphere-Oriented Training (SOT) to incorporate the GPM into the training process and further improve cross-domain performances. Experimental results demonstrate that the AGG effectively alleviate the overfitting problem and consistently improves the cross-domain gaze estimation accuracy in 12 cross-domain settings without requiring any target domain data. The insight from the Analytical Gaze Generalization framework has the potential to benefit other regression tasks with physical meanings.

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Curriculum Point Prompting for Weakly-Supervised Referring Image Segmentation

http://arxiv.org/abs/2404.11998

Qiyuan Dai, Sibei Yang

Referring image segmentation (RIS) aims to precisely segment referents in images through corresponding natural language expressions yet relying on cost-intensive mask annotations. Weakly supervised RIS thus learns from image-text pairs to pixel-level semantics which is challenging for segmenting fine-grained masks. A natural approach to enhancing segmentation precision is to empower weakly supervised RIS with the image segmentation foundation model SAM. Nevertheless we observe that simply integrating SAM yields limited benefits and can even lead to performance regression due to the inevitable noise issues and challenges in excessive focus on object parts. In this paper we present an innovative framework Point PrompTing (PPT) incorporated with the proposed multi-source curriculum learning strategy to address these challenges. Specifically the core of PPT is a point generator that not only harnesses CLIP's text-image alignment capability and SAM's powerful mask generation ability but also generates negative point prompts to address the noisy and excessive focus issues inherently and effectively. In addition we introduce a curriculum learning strategy with object-centric images to help PPT gradually learn from simpler yet precise semantic alignment to more complex RIS. Experiments demonstrate that our PPT significantly and consistently outperforms prior weakly supervised techniques on mIoU by 11.34% 14.14% and 6.97% across RefCOCO RefCOCO+ and G-Ref respectively.

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EventDance: Unsupervised Source-free Cross-modal Adaptation for Event-based Object Recognition

http://arxiv.org/abs/2403.14082

Xu Zheng, Lin Wang

In this paper we make the first attempt at achieving the cross-modal (i.e. image-to-events) adaptation for event-based object recognition without accessing any labeled source image data owning to privacy and commercial issues. Tackling this novel problem is non-trivial due to the novelty of event cameras and the distinct modality gap between images and events. In particular as only the source model is available a hurdle is how to extract the knowledge from the source model by only using the unlabeled target event data while achieving knowledge transfer. To this end we propose a novel framework dubbed EventDance for this unsupervised source-free cross-modal adaptation problem. Importantly inspired by event-to-video reconstruction methods we propose a reconstruction-based modality bridging (RMB) module which reconstructs intensity frames from events in a self-supervised manner. This makes it possible to build up the surrogate images to extract the knowledge (i.e. labels) from the source model. We then propose a multi-representation knowledge adaptation (MKA) module that transfers the knowledge to target models learning events with multiple representation types for fully exploring the spatiotemporal information of events. The two modules connecting the source and target models are mutually updated so as to achieve the best performance. Experiments on three benchmark datasets with two adaption settings show that EventDance is on par with prior methods utilizing the source data.

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CycleINR: Cycle Implicit Neural Representation for Arbitrary-Scale Volumetric Super-Resolution of Medical Data

http://arxiv.org/abs/2404.04878

Wei Fang, Yuxing Tang, Heng Guo, Mingze Yuan, Tony C. W. Mok, Ke Yan, Jiawen Yao, Xin Chen, Zaiyi Liu, Le Lu, Ling Zhang, Minfeng Xu

In the realm of medical 3D data such as CT and MRI images prevalent anisotropic resolution is characterized by high intra-slice but diminished inter-slice resolution. The lowered resolution between adjacent slices poses challenges hindering optimal viewing experiences and impeding the development of robust downstream analysis algorithms. Various volumetric super-resolution algorithms aim to surmount these challenges enhancing inter-slice resolution and overall 3D medical imaging quality. However existing approaches confront inherent challenges: 1) often tailored to specific upsampling factors lacking flexibility for diverse clinical scenarios; 2) newly generated slices frequently suffer from over-smoothing degrading fine details and leading to inter-slice inconsistency. In response this study presents CycleINR a novel enhanced Implicit Neural Representation model for 3D medical data volumetric super-resolution. Leveraging the continuity of the learned implicit function the CycleINR model can achieve results with arbitrary up-sampling rates eliminating the need for separate training. Additionally we enhance the grid sampling in CycleINR with a local attention mechanism and mitigate over-smoothing by integrating cycle-consistent loss. We introduce a new metric Slice-wise Noise Level Inconsistency (SNLI) to quantitatively assess inter-slice noise level inconsistency. The effectiveness of our approach is demonstrated through image quality evaluations on an in-house dataset and a downstream task analysis on the Medical Segmentation Decathlon liver tumor dataset.

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Boosting Image Restoration via Priors from Pre-trained Models

http://arxiv.org/abs/2403.06793

Xiaogang Xu, Shu Kong, Tao Hu, Zhe Liu, Hujun Bao

Pre-trained models with large-scale training data such as CLIP and Stable Diffusion have demonstrated remarkable performance in various high-level computer vision tasks such as image understanding and generation from language descriptions. Yet their potential for low-level tasks such as image restoration remains relatively unexplored. In this paper we explore such models to enhance image restoration. As off-the-shelf features (OSF) from pre-trained models do not directly serve image restoration we propose to learn an additional lightweight module called Pre-Train-Guided Refinement Module (PTG-RM) to refine restoration results of a target restoration network with OSF. PTG-RM consists of two components Pre-Train-Guided Spatial-Varying Enhancement (PTG-SVE) and Pre-Train-Guided Channel-Spatial Attention (PTG-CSA). PTG-SVE enables optimal short- and long-range neural operations while PTG-CSA enhances spatial-channel attention for restoration-related learning. Extensive experiments demonstrate that PTG-RM with its compact size (<1M parameters) effectively enhances restoration performance of various models across different tasks including low-light enhancement deraining deblurring and denoising.

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VRetouchEr: Learning Cross-frame Feature Interdependence with Imperfection Flow for Face Retouching in Videos

Wen Xue, Le Jiang, Lianxin Xie, Si Wu, Yong Xu, Hau San Wong

Face Video Retouching is a complex task that often requires labor-intensive manual editing. Conventional image retouching methods perform less satisfactorily in terms of generalization performance and stability when applied to videos without exploiting the correlation among frames. To address this issue we propose a Video Retouching transformEr to remove facial imperfections in videos which is referred to as VRetouchEr. Specifically we estimate the apparent motion of imperfections between two consecutive frames and the resulting displacement vectors are used to refine the imperfection map which is synthesized from the current frame together with the corresponding encoder features. The flow-based imperfection refinement is critical for precise and stable retouching across frames. To leverage the temporal contextual information we inject the refined imperfection map into each transformer block for multi-frame masked attention computation such that we can capture the interdependence between the current frame and multiple reference frames. As a result the imperfection regions can be replaced with normal skin with high fidelity while at the same time keeping the other regions unchanged. Extensive experiments are performed to verify the superiority of VRetouchEr over state-of-the-art image retouching methods in terms of fidelity and stability.

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Transferable Structural Sparse Adversarial Attack Via Exact Group Sparsity Training

Di Ming, Peng Ren, Yunlong Wang, Xin Feng

Deep neural networks (DNNs) are vulnerable to highly transferable adversarial attacks. Especially many studies have shown that sparse attacks pose a significant threat to DNNs on account of their exceptional imperceptibility. Current sparse attack methods mostly limit only the magnitude and number of perturbations while generally overlooking the location of the perturbations resulting in decreased performances on attack transferability. A subset of studies indicates that perturbations existing in the significant regions with rich classification-relevant features are more effective. Leveraging this insight we introduce the structural sparsity constraint in the framework of generative models to limit the perturbation positions. To ensure that the perturbations are generated towards classification-relevant regions we propose an exact group sparsity training method to learn pixel-level and group-level sparsity. For purpose of improving the effectiveness of sparse training we further put forward masked quantization network and multi-stage optimization algorithm in the training process. Utilizing CNNs as surrogate models extensive experiments demonstrate that our method has higher transferability in image classification attack compared to state-of-the-art methods at approximately same sparsity levels. In cross-model ViT object detection and semantic segmentation attack tasks we also achieve a better attack success rate. Code is available at https://github.com/MisterRpeng/EGS-TSSA.

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Holistic Autonomous Driving Understanding by Bird's-Eye-View Injected Multi-Modal Large Models

Xinpeng Ding, Jianhua Han, Hang Xu, Xiaodan Liang, Wei Zhang, Xiaomeng Li

The rise of multimodal large language models (MLLMs) has spurred interest in language-based driving tasks. However existing research typically focuses on limited tasks and often omits key multi-view and temporal information which is crucial for robust autonomous driving. To bridge these gaps we introduce NuInstruct a novel dataset with 91K multi-view video-QA pairs across 17 subtasks where each task demands holistic information (e.g. temporal multi-view and spatial) significantly elevating the challenge level. To obtain NuInstruct we propose a novel SQL-based method to generate instruction-response pairs automatically which is inspired by the driving logical progression of humans. We further present BEV-InMLLM an end-to-end method for efficiently deriving instruction-aware Bird's-Eye-View (BEV) features language-aligned for large language models. BEV-InMLLM integrates multi-view spatial awareness and temporal semantics to enhance MLLMs' capabilities on NuInstruct tasks. Moreover our proposed BEV injection module is a plug-and-play method for existing MLLMs. Our experiments on NuInstruct demonstrate that BEV-InMLLM significantly outperforms existing MLLMs e.g 9% improvement on various tasks. We release our NuInstruct at https://github.com/xmed-lab/NuInstruct.

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Arbitrary-Scale Image Generation and Upsampling using Latent Diffusion Model and Implicit Neural Decoder

http://arxiv.org/abs/2403.10255

Jinseok Kim, Tae-Kyun Kim

Super-resolution (SR) and image generation are important tasks in computer vision and are widely adopted in real-world applications. Most existing methods however generate images only at fixed-scale magnification and suffer from over-smoothing and artifacts. Additionally they do not offer enough diversity of output images nor image consistency at different scales. Most relevant work applied Implicit Neural Representation (INR) to the denoising diffusion model to obtain continuous-resolution yet diverse and high-quality SR results. Since this model operates in the image space the larger the resolution of image is produced the more memory and inference time is required and it also does not maintain scale-specific consistency. We propose a novel pipeline that can super-resolve an input image or generate from a random noise a novel image at arbitrary scales. The method consists of a pretrained auto-encoder a latent diffusion model and an implicit neural decoder and their learning strategies. The proposed method adopts diffusion processes in a latent space thus efficient yet aligned with output image space decoded by MLPs at arbitrary scales. More specifically our arbitrary-scale decoder is designed by the symmetric decoder w/o up-scaling from the pretrained auto-encoder and Local Implicit Image Function (LIIF) in series. The latent diffusion process is learnt by the denoising and the alignment losses jointly. Errors in output images are backpropagated via the fixed decoder improving the quality of output images. In the extensive experiments using multiple public benchmarks on the two tasks i.e. image super-resolution and novel image generation at arbitrary scales the proposed method outperforms relevant methods in metrics of image quality diversity and scale consistency. It is significantly better than the relevant prior-art in the inference speed and memory usage.

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