axxkaya/UVT-Terminological-based-Vision-Tasks

Explanatory Instructions: Towards Unified Vision Tasks Understanding and Zero-shot Generalization

Computer Vision (CV) has yet to fully achieve the zero-shot task generalization observed in Natural Language Processing (NLP), despite following many of the milestones established in NLP, such as large transformer models, extensive pre-training, and the auto-regression paradigm, among others. In this paper, we rethink the reality that CV adopts discrete and terminological task definitions (e.g., "image segmentation"), and conjecture it is a key barrier that hampers zero-shot task generalization. Our hypothesis is that without truly understanding previously-seen tasks--due to these terminological definitions--deep models struggle to generalize to novel tasks. To verify this, we introduce Explanatory Instructions, which provide an intuitive way to define CV task objectives through detailed linguistic transformations from input images to outputs. We create a large-scale dataset comprising 12 million "image input → explanatory instruction → output" triplets, and train an auto-regressive-based vision-language model (AR-based VLM) that takes both images and explanatory instructions as input. By learning to follow these instructions, the AR-based VLM achieves instruction-level zero-shot capabilities for previously-seen tasks and demonstrates strong zero-shot generalization for unseen CV tasks.

📃Paper | 💻Github | 📚Dataset (Explanatory-based Vison Tasks) | 📚Dataset (Terminological-based Vision Tasks) | 🤗 Model (UVT-7B-448)(Not availabe now.)

Dataset Columns

This sub-dataset only contains data for Terminological-based Vison Tasks, including Image Restoration, Deraining, Dehazing, Desnowing, Object Detection, Style Transfer, Depth Estimation, Surface Normal Estimation, Pose Estimation, Semantic Segmentation, Holistically-Nested Edge (HED) Boundary to Image and inverse dense image prediction tasks (e.g., Pose-to-Image, Depth-to-Image, Segmentation-to-Image). (some all still uploading)

This dataset contains the following columns:

• _id: "dtype": "int32", "description": "Unique identifier".
• TASK: "dtype": "string".
• Image_A: "dtype": "image", "description": "Binary image data".
• Image_B: "dtype": "image", "description": "Binary image data".
• Image_C: "dtype": "image", "description": "Binary image data or None".
• Task_Descriptions_from_A_to_B": "dtype": "string".
• Task_Descriptions_from_A_to_C": "dtype": "string".
• Task_Descriptions_from_B_to_A": "dtype": "string".
• Task_Descriptions_from_B_to_C": "dtype": "string".
• Task_Descriptions_from_C_to_A": "dtype": "string".
• Task_Descriptions_from_C_to_B": "dtype": "string".

Citation

If you find our paper useful, please cite us with

@article{shen2024explanatory,
  title={Explanatory Instructions: Towards Unified Vision Tasks Understanding and Zero-shot Generalization},
  author={Shen, Yang and Wei, Xiu-Shen and Sun, Yifan and Song, Yuxin and Yuan, Tao and Jin, Jian and Xu, Heyang and Yao, Yazhou and Ding, Errui},
  journal={arXiv preprint arXiv:2412.18525},
  year={2024}
}