De-fine: Decomposing and Refining Visual Programs with Auto-Feedback
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909278605934592 |
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| author | Gao, Minghe Li, Juncheng Fei, Hao Pang, Liang Ji, Wei Wang, Guoming Lv, Zheqi Zhang, Wenqiao Tang, Siliang Zhuang, Yueting |
| author_facet | Gao, Minghe Li, Juncheng Fei, Hao Pang, Liang Ji, Wei Wang, Guoming Lv, Zheqi Zhang, Wenqiao Tang, Siliang Zhuang, Yueting |
| contents | Visual programming, a modular and generalizable paradigm, integrates different modules and Python operators to solve various vision-language tasks. Unlike end-to-end models that need task-specific data, it advances in performing visual processing and reasoning in an unsupervised manner. Current visual programming methods generate programs in a single pass for each task where the ability to evaluate and optimize based on feedback, unfortunately, is lacking, which consequentially limits their effectiveness for complex, multi-step problems. Drawing inspiration from benders decomposition, we introduce De-fine, a training-free framework that automatically decomposes complex tasks into simpler subtasks and refines programs through auto-feedback. This model-agnostic approach can improve logical reasoning performance by integrating the strengths of multiple models. Our experiments across various visual tasks show that De-fine creates more robust programs. Moreover, viewing each feedback module as an independent agent will yield fresh prospects for the field of agent research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_12890 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | De-fine: Decomposing and Refining Visual Programs with Auto-Feedback Gao, Minghe Li, Juncheng Fei, Hao Pang, Liang Ji, Wei Wang, Guoming Lv, Zheqi Zhang, Wenqiao Tang, Siliang Zhuang, Yueting Computer Vision and Pattern Recognition Visual programming, a modular and generalizable paradigm, integrates different modules and Python operators to solve various vision-language tasks. Unlike end-to-end models that need task-specific data, it advances in performing visual processing and reasoning in an unsupervised manner. Current visual programming methods generate programs in a single pass for each task where the ability to evaluate and optimize based on feedback, unfortunately, is lacking, which consequentially limits their effectiveness for complex, multi-step problems. Drawing inspiration from benders decomposition, we introduce De-fine, a training-free framework that automatically decomposes complex tasks into simpler subtasks and refines programs through auto-feedback. This model-agnostic approach can improve logical reasoning performance by integrating the strengths of multiple models. Our experiments across various visual tasks show that De-fine creates more robust programs. Moreover, viewing each feedback module as an independent agent will yield fresh prospects for the field of agent research. |
| title | De-fine: Decomposing and Refining Visual Programs with Auto-Feedback |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2311.12890 |