Unifying Image Processing as Visual Prompting Question Answering
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866914687371706368 |
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| author | Liu, Yihao Chen, Xiangyu Ma, Xianzheng Wang, Xintao Zhou, Jiantao Qiao, Yu Dong, Chao |
| author_facet | Liu, Yihao Chen, Xiangyu Ma, Xianzheng Wang, Xintao Zhou, Jiantao Qiao, Yu Dong, Chao |
| contents | Image processing is a fundamental task in computer vision, which aims at enhancing image quality and extracting essential features for subsequent vision applications. Traditionally, task-specific models are developed for individual tasks and designing such models requires distinct expertise. Building upon the success of large language models (LLMs) in natural language processing (NLP), there is a similar trend in computer vision, which focuses on developing large-scale models through pretraining and in-context learning. This paradigm shift reduces the reliance on task-specific models, yielding a powerful unified model to deal with various tasks. However, these advances have predominantly concentrated on high-level vision tasks, with less attention paid to low-level vision tasks. To address this issue, we propose a universal model for general image processing that covers image restoration, image enhancement, image feature extraction tasks, etc. Our proposed framework, named PromptGIP, unifies these diverse image processing tasks within a universal framework. Inspired by NLP question answering (QA) techniques, we employ a visual prompting question answering paradigm. Specifically, we treat the input-output image pair as a structured question-answer sentence, thereby reprogramming the image processing task as a prompting QA problem. PromptGIP can undertake diverse cross-domain tasks using provided visual prompts, eliminating the need for task-specific finetuning. Our methodology offers a universal and adaptive solution to general image processing. While PromptGIP has demonstrated a certain degree of out-of-domain task generalization capability, further research is expected to fully explore its more powerful emergent generalization. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2310_10513 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Unifying Image Processing as Visual Prompting Question Answering Liu, Yihao Chen, Xiangyu Ma, Xianzheng Wang, Xintao Zhou, Jiantao Qiao, Yu Dong, Chao Computer Vision and Pattern Recognition Image and Video Processing Image processing is a fundamental task in computer vision, which aims at enhancing image quality and extracting essential features for subsequent vision applications. Traditionally, task-specific models are developed for individual tasks and designing such models requires distinct expertise. Building upon the success of large language models (LLMs) in natural language processing (NLP), there is a similar trend in computer vision, which focuses on developing large-scale models through pretraining and in-context learning. This paradigm shift reduces the reliance on task-specific models, yielding a powerful unified model to deal with various tasks. However, these advances have predominantly concentrated on high-level vision tasks, with less attention paid to low-level vision tasks. To address this issue, we propose a universal model for general image processing that covers image restoration, image enhancement, image feature extraction tasks, etc. Our proposed framework, named PromptGIP, unifies these diverse image processing tasks within a universal framework. Inspired by NLP question answering (QA) techniques, we employ a visual prompting question answering paradigm. Specifically, we treat the input-output image pair as a structured question-answer sentence, thereby reprogramming the image processing task as a prompting QA problem. PromptGIP can undertake diverse cross-domain tasks using provided visual prompts, eliminating the need for task-specific finetuning. Our methodology offers a universal and adaptive solution to general image processing. While PromptGIP has demonstrated a certain degree of out-of-domain task generalization capability, further research is expected to fully explore its more powerful emergent generalization. |
| title | Unifying Image Processing as Visual Prompting Question Answering |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2310.10513 |