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CVPR2024

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CVPR2024-papers

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Finding Lottery Tickets in Vision Models via Data-driven Spectral Foresight Pruning

Leonardo Iurada, Marco Ciccone, Tatiana Tommasi

Recent advances in neural network pruning have shown how it is possible to reduce the computational costs and memory demands of deep learning models before training. We focus on this framework and propose a new pruning at initialization algorithm that leverages the Neural Tangent Kernel (NTK) theory to align the training dynamics of the sparse network with that of the dense one. Specifically we show how the usually neglected data-dependent component in the NTK's spectrum can be taken into account by providing an analytical upper bound to the NTK's trace obtained by decomposing neural networks into individual paths. This leads to our Path eXclusion (PX) a foresight pruning method designed to preserve the parameters that mostly influence the NTK's trace. PX is able to find lottery tickets (i.e. good paths) even at high sparsity levels and largely reduces the need for additional training. When applied to pre-trained models it extracts subnetworks directly usable for several downstream tasks resulting in performance comparable to those of the dense counterpart but with substantial cost and computational savings.

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InNeRF360: Text-Guided 3D-Consistent Object Inpainting on 360-degree Neural Radiance Fields

http://arxiv.org/abs/2305.15094

Dongqing Wang, Tong Zhang, Alaa Abboud, Sabine Süsstrunk

We propose InNeRF360 an automatic system that accurately removes text-specified objects from 360-degree Neural Radiance Fields (NeRF). The challenge is to effectively remove objects while inpainting perceptually consistent content for the missing regions which is particularly demanding for existing NeRF models due to their implicit volumetric representation. Moreover unbounded scenes are more prone to floater artifacts in the inpainted region than frontal-facing scenes as the change of object appearance and background across views is more sensitive to inaccurate segmentations and inconsistent inpainting. With a trained NeRF and a text description our method efficiently removes specified objects and inpaints visually consistent content without artifacts. We apply depth-space warping to enforce consistency across multiview text-encoded segmentations and then refine the inpainted NeRF model using perceptual priors and 3D diffusion-based geometric priors to ensure visual plausibility. Through extensive experiments in segmentation and inpainting on 360-degree and frontal-facing NeRFs we show that InNeRF360 is effective and enhances NeRF's editability. Project page: https://ivrl.github.io/InNeRF360.

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Neural Implicit Representation for Building Digital Twins of Unknown Articulated Objects

http://arxiv.org/abs/2404.01440

Yijia Weng, Bowen Wen, Jonathan Tremblay, Valts Blukis, Dieter Fox, Leonidas Guibas, Stan Birchfield

We address the problem of building digital twins of unknown articulated objects from two RGBD scans of the object at different articulation states. We decompose the problem into two stages each addressing distinct aspects. Our method first reconstructs object-level shape at each state then recovers the underlying articulation model including part segmentation and joint articulations that associate the two states. By explicitly modeling point-level correspondences and exploiting cues from images 3D reconstructions and kinematics our method yields more accurate and stable results compared to prior work. It also handles more than one movable part and does not rely on any object shape or structure priors. Project page: https://github.com/NVlabs/DigitalTwinArt

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Progressive Semantic-Guided Vision Transformer for Zero-Shot Learning

http://arxiv.org/abs/2404.07713

Shiming Chen, Wenjin Hou, Salman Khan, Fahad Shahbaz Khan

Zero-shot learning (ZSL) recognizes the unseen classes by conducting visual-semantic interactions to transfer semantic knowledge from seen classes to unseen ones supported by semantic information (e.g. attributes). However existing ZSL methods simply extract visual features using a pre-trained network backbone (i.e. CNN or ViT) which fail to learn matched visual-semantic correspondences for representing semantic-related visual features as lacking of the guidance of semantic information resulting in undesirable visual-semantic interactions. To tackle this issue we propose a progressive semantic-guided vision transformer for zero-shot learning (dubbed ZSLViT). ZSLViT mainly considers two properties in the whole network: i) discover the semantic-related visual representations explicitly and ii) discard the semantic-unrelated visual information. Specifically we first introduce semantic-embedded token learning to improve the visual-semantic correspondences via semantic enhancement and discover the semantic-related visual tokens explicitly with semantic-guided token attention. Then we fuse low semantic-visual correspondence visual tokens to discard the semantic-unrelated visual information for visual enhancement. These two operations are integrated into various encoders to progressively learn semantic-related visual representations for accurate visual-semantic interactions in ZSL. The extensive experiments show that our ZSLViT achieves significant performance gains on three popular benchmark datasets i.e. CUB SUN and AWA2.

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IS-Fusion: Instance-Scene Collaborative Fusion for Multimodal 3D Object Detection

Junbo Yin, Jianbing Shen, Runnan Chen, Wei Li, Ruigang Yang, Pascal Frossard, Wenguan Wang

Bird's eye view (BEV) representation has emerged as a dominant solution for describing 3D space in autonomous driving scenarios. However objects in the BEV representation typically exhibit small sizes and the associated point cloud context is inherently sparse which leads to great challenges for reliable 3D perception. In this paper we propose IS-Fusion an innovative multimodal fusion framework that jointly captures the Instance- and Scene-level contextual information. IS-Fusion essentially differs from existing approaches that only focus on the BEV scene-level fusion by explicitly incorporating instance-level multimodal information thus facilitating the instance-centric tasks like 3D object detection. It comprises a Hierarchical Scene Fusion (HSF) module and an Instance-Guided Fusion (IGF) module. HSF applies Point-to-Grid and Grid-to-Region transformers to capture the multimodal scene context at different granularities. IGF mines instance candidates explores their relationships and aggregates the local multimodal context for each instance. These instances then serve as guidance to enhance the scene feature and yield an instance-aware BEV representation. On the challenging nuScenes benchmark IS-Fusion outperforms all the published multimodal works to date.

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Building Bridges across Spatial and Temporal Resolutions: Reference-Based Super-Resolution via Change Priors and Conditional Diffusion Model

http://arxiv.org/abs/2403.17460

Runmin Dong, Shuai Yuan, Bin Luo, Mengxuan Chen, Jinxiao Zhang, Lixian Zhang, Weijia Li, Juepeng Zheng, Haohuan Fu

Reference-based super-resolution (RefSR) has the potential to build bridges across spatial and temporal resolutions of remote sensing images. However existing RefSR methods are limited by the faithfulness of content reconstruction and the effectiveness of texture transfer in large scaling factors. Conditional diffusion models have opened up new opportunities for generating realistic high-resolution images but effectively utilizing reference images within these models remains an area for further exploration. Furthermore content fidelity is difficult to guarantee in areas without relevant reference information. To solve these issues we propose a change-aware diffusion model named Ref-Diff for RefSR using the land cover change priors to guide the denoising process explicitly. Specifically we inject the priors into the denoising model to improve the utilization of reference information in unchanged areas and regulate the reconstruction of semantically relevant content in changed areas. With this powerful guidance we decouple the semantics-guided denoising and reference texture-guided denoising processes to improve the model performance. Extensive experiments demonstrate the superior effectiveness and robustness of the proposed method compared with state-of-the-art RefSR methods in both quantitative and qualitative evaluations. The code and data are available at https://github.com/dongrunmin/RefDiff.

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Vanishing-Point-Guided Video Semantic Segmentation of Driving Scenes

http://arxiv.org/abs/2401.15261

Diandian Guo, Deng-Ping Fan, Tongyu Lu, Christos Sakaridis, Luc Van Gool

The estimation of implicit cross-frame correspondences and the high computational cost have long been major challenges in video semantic segmentation (VSS) for driving scenes. Prior works utilize keyframes feature propagation or cross-frame attention to address these issues. By contrast we are the first to harness vanishing point (VP) priors for more effective segmentation. Intuitively objects near VPs (i.e. away from the vehicle) are less discernible. Moreover they tend to move radially away from the VP over time in the usual case of a forward-facing camera a straight road and linear forward motion of the vehicle. Our novel efficient network for VSS named VPSeg incorporates two modules that utilize exactly this pair of static and dynamic VP priors: sparse-to-dense feature mining (DenseVP) and VP-guided motion fusion (MotionVP). MotionVP employs VP-guided motion estimation to establish explicit correspondences across frames and help attend to the most relevant features from neighboring frames while DenseVP enhances weak dynamic features in distant regions around VPs. These modules operate within a context-detail framework which separates contextual features from high-resolution local features at different input resolutions to reduce computational costs. Contextual and local features are integrated through contextualized motion attention (CMA) for the final prediction. Extensive experiments on two popular driving segmentation benchmarks Cityscapes and ACDC demonstrate that VPSeg outperforms previous SOTA methods with only modest computational overhead.

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Enhancing Intrinsic Features for Debiasing via Investigating Class-Discerning Common Attributes in Bias-Contrastive Pair

http://arxiv.org/abs/2404.19250

Jeonghoon Park, Chaeyeon Chung, Jaegul Choo

In the image classification task deep neural networks frequently rely on bias attributes that are spuriously correlated with a target class in the presence of dataset bias resulting in degraded performance when applied to data without bias attributes. The task of debiasing aims to compel classifiers to learn intrinsic attributes that inherently define a target class rather than focusing on bias attributes. While recent approaches mainly focus on emphasizing the learning of data samples without bias attributes (i.e. bias-conflicting samples) compared to samples with bias attributes (i.e. bias-aligned samples) they fall short of directly guiding models where to focus for learning intrinsic features. To address this limitation this paper proposes a method that provides the model with explicit spatial guidance that indicates the region of intrinsic features. We first identify the intrinsic features by investigating the class-discerning common features between a bias-aligned (BA) sample and a bias-conflicting (BC) sample (i.e. bias-contrastive pair). Next we enhance the intrinsic features in the BA sample that are relatively under-exploited for prediction compared to the BC sample. To construct the bias-contrastive pair without using bias information we introduce a bias-negative score that distinguishes BC samples from BA samples employing a biased model. The experiments demonstrate that our method achieves state-of-the-art performance on synthetic and real-world datasets with various levels of bias severity.

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LAMP: Learn A Motion Pattern for Few-Shot Video Generation

Ruiqi Wu, Liangyu Chen, Tong Yang, Chunle Guo, Chongyi Li, Xiangyu Zhang

In this paper we present a few-shot text-to-video framework LAMP which enables a text-to-image diffusion model to Learn A specific Motion Pattern with 8 16 videos on a single GPU. Unlike existing methods which require a large number of training resources or learn motions that are precisely aligned with template videos it achieves a trade-off between the degree of generation freedom and the resource costs for model training. Specifically we design a motion-content decoupled pipeline that uses an off-the-shelf text-to-image model for content generation so that our tuned video diffusion model mainly focuses on motion learning. The well-developed text-to-image techniques can provide visually pleasing and diverse content as generation conditions which highly improves video quality and generation freedom. To capture the features of temporal dimension we expand the pre-trained 2D convolution layers of the T2I model to our novel temporal-spatial motion learning layers and modify the attention blocks to the temporal level. Additionally we develop an effective inference trick shared-noise sampling which can improve the stability of videos without computational costs. Our method can also be flexibly applied to other tasks e.g. real-world image animation and video editing. Extensive experiments demonstrate that LAMP can effectively learn the motion pattern on limited data and generate high-quality videos. The code and models are available at https://rq-wu.github.io/projects/LAMP.

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Compositional Chain-of-Thought Prompting for Large Multimodal Models

http://arxiv.org/abs/2311.17076

Chancharik Mitra, Brandon Huang, Trevor Darrell, Roei Herzig

The combination of strong visual backbones and Large Language Model (LLM) reasoning has led to Large Multimodal Models (LMMs) becoming the current standard for a wide range of vision and language (VL) tasks. However recent research has shown that even the most advanced LMMs still struggle to capture aspects of compositional visual reasoning such as attributes and relationships between objects. One solution is to utilize scene graphs (SGs)---a formalization of objects and their relations and attributes that has been extensively used as a bridge between the visual and textual domains. Yet scene graph data requires scene graph annotations which are expensive to collect and thus not easily scalable. Moreover finetuning an LMM based on SG data can lead to catastrophic forgetting of the pretraining objective. To overcome this inspired by chain-of-thought methods we propose Compositional Chain-of-Thought (CCoT) a novel zero-shot Chain-of-Thought prompting method that utilizes SG representations in order to extract compositional knowledge from an LMM. Specifically we first generate an SG using the LMM and then use that SG in the prompt to produce a response. Through extensive experiments we find that the proposed CCoT approach not only improves LMM performance on several vision and language VL compositional benchmarks but also improves the performance of several popular LMMs on general multimodal benchmarks without the need for fine-tuning or annotated ground-truth SGs. Code: https://github.com/chancharikmitra/CCoT

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Diffusion Time-step Curriculum for One Image to 3D Generation

http://arxiv.org/abs/2404.04562

Xuanyu Yi, Zike Wu, Qingshan Xu, Pan Zhou, Joo-Hwee Lim, Hanwang Zhang

Score distillation sampling (SDS) has been widely adopted to overcome the absence of unseen views in reconstructing 3D objects from a single image. It leverages pre-trained 2D diffusion models as teacher to guide the reconstruction of student 3D models. Despite their remarkable success SDS-based methods often encounter geometric artifacts and texture saturation. We find out the crux is the overlooked indiscriminate treatment of diffusion time-steps during optimization: it unreasonably treats the student-teacher knowledge distillation to be equal at all time-steps and thus entangles coarse-grained and fine-grained modeling. Therefore we propose the Diffusion Time-step Curriculum one-image-to-3D pipeline (DTC123) which involves both the teacher and student models collaborating with the time-step curriculum in a coarse-to-fine manner. Extensive experiments on NeRF4 RealFusion15 GSO and Level50 benchmark demonstrate that DTC123 can produce multi-view consistent high-quality and diverse 3D assets. Codes and more generation demos will be released in https://github.com/yxymessi/DTC123.

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Language-driven Object Fusion into Neural Radiance Fields with Pose-Conditioned Dataset Updates

http://arxiv.org/abs/2309.11281

Ka Chun Shum, Jaeyeon Kim, Binh-Son Hua, Duc Thanh Nguyen, Sai-Kit Yeung

Neural radiance field (NeRF) is an emerging technique for 3D scene reconstruction and modeling. However current NeRF-based methods are limited in the capabilities of adding or removing objects. This paper fills the aforementioned gap by proposing a new language-driven method for object manipulation in NeRFs through dataset updates. Specifically to insert an object represented by a set of multi-view images into a background NeRF we use a text-to-image diffusion model to blend the object into the given background across views. The generated images are then used to update the NeRF so that we can render view-consistent images of the object within the background. To ensure view consistency we propose a dataset update strategy that prioritizes the radiance field training based on camera poses in a pose-ordered manner. We validate our method in two case studies: object insertion and object removal. Experimental results show that our method can generate photo-realistic results and achieves state-of-the-art performance in NeRF editing.

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Adaptive Hyper-graph Aggregation for Modality-Agnostic Federated Learning

Fan Qi, Shuai Li

In Federated Learning (FL) the issue of statistical data heterogeneity has been a significant challenge to the field's ongoing development. This problem is further exacerbated when clients' data vary in modalities. In response to these issues of statistical heterogeneity and modality incompatibility we propose the Adaptive Hyper-graph Aggregation framework a novel solution for Modality-Agnostic Federated Learning. We design a Modular Architecture for Local Model with single modality setting the stage for efficient intra-modality sharing and inter-modality complementarity. An innovative Global Consensus Prototype Enhancer is crafted to assimilate and broadcast global consensus knowledge within the network. At the core of our approach lies the Adaptive Hyper-graph Learning Strategy which effectively tackles the inherent challenges of modality incompatibility and statistical heterogeneity within federated learning environments accomplishing this adaptively even without the server being aware of the clients' modalities. Our approach tested on three multimodal benchmark datasets demonstrated strong performance across diverse data distributions affirming its effectiveness in multimodal federated learning.

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SPIN: Simultaneous Perception Interaction and Navigation

http://arxiv.org/abs/2405.07991

Shagun Uppal, Ananye Agarwal, Haoyu Xiong, Kenneth Shaw, Deepak Pathak

While there has been remarkable progress recently in the fields of manipulation and locomotion mobile manipulation remains a long-standing challenge. Compared to locomotion or static manipulation a mobile system must make a diverse range of long-horizon tasks feasible in unstructured and dynamic environments. While the applications are broad and interesting there are a plethora of challenges in developing these systems such as coordination between the base and arm reliance on onboard perception for perceiving and interacting with the environment and most importantly simultaneously integrating all these parts together. Prior works approach the problem using disentangled modular skills for mobility and manipulation that are trivially tied together. This causes several limitations such as compounding errors delays in decision-making and no whole-body coordination. In this work we present a reactive mobile manipulation framework that uses an active visual system to consciously perceive and react to its environment. Similar to how humans leverage whole-body and hand-eye coordination we develop a mobile manipulator that exploits its ability to move and see more specifically -- to move in order to see and to see in order to move. This allows it to not only move around and interact with its environment but also choose "when" to perceive "what" using an active visual system. We observe that such an agent learns to navigate around complex cluttered scenarios while displaying agile whole-body coordination using only ego-vision without needing to create environment maps. Videos are available at https://spin-robot.github.io

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DREAM: Diffusion Rectification and Estimation-Adaptive Models

http://arxiv.org/abs/2312.00210

Jinxin Zhou, Tianyu Ding, Tianyi Chen, Jiachen Jiang, Ilya Zharkov, Zhihui Zhu, Luming Liang

We present DREAM a novel training framework representing Diffusion Rectification and Estimation-Adaptive Models requiring minimal code changes (just three lines) yet significantly enhancing the alignment of training with sampling in diffusion models. DREAM features two components: diffusion rectification which adjusts training to reflect the sampling process and estimation adaptation which balances perception against distortion. When applied to image super-resolution (SR) DREAM adeptly navigates the tradeoff between minimizing distortion and preserving high image quality. Experiments demonstrate DREAM's superiority over standard diffusion-based SR methods showing a to faster training convergence and a to reduction in necessary sampling steps to achieve comparable or superior results. We hope DREAM will inspire a rethinking of diffusion model training paradigms.

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Exploring the Potential of Large Foundation Models for Open-Vocabulary HOI Detection

http://arxiv.org/abs/2404.06194

Ting Lei, Shaofeng Yin, Yang Liu

Open-vocabulary human-object interaction (HOI) detection which is concerned with the problem of detecting novel HOIs guided by natural language is crucial for understanding human-centric scenes. However prior zero-shot HOI detectors often employ the same levels of feature maps to model HOIs with varying distances leading to suboptimal performance in scenes containing human-object pairs with a wide range of distances. In addition these detectors primarily rely on category names and overlook the rich contextual information that language can provide which is essential for capturing open vocabulary concepts that are typically rare and not well-represented by category names alone. In this paper we introduce a novel end-to-end open vocabulary HOI detection framework with conditional multi-level decoding and fine-grained semantic enhancement (CMD-SE) harnessing the potential of Visual-Language Models (VLMs). Specifically we propose to model human-object pairs with different distances with different levels of feature maps by incorporating a soft constraint during the bipartite matching process. Furthermore by leveraging large language models (LLMs) such as GPT models we exploit their extensive world knowledge to generate descriptions of human body part states for various interactions. Then we integrate the generalizable and fine-grained semantics of human body parts to improve interaction recognition. Experimental results on two datasets SWIG-HOI and HICO-DET demonstrate that our proposed method achieves state-of-the-art results in open vocabulary HOI detection. The code and models are available at https://github.com/ltttpku/CMD-SE-release.

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