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arXiv · 2311.12890

De-fine: Decomposing and Refining Visual Programs with Auto-Feedback

Minghe Gao·Juncheng Li·Hao Fei·Liang Pang·Wei Ji·Guoming Wang·Zheqi Lv·Wenqiao Zhang·Siliang Tang·Yueting Zhuang
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Abstract

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.

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Minghe Gao, Juncheng Li, Hao Fei, Liang Pang, Wei Ji, Guoming Wang, Zheqi Lv, Wenqiao Zhang, Siliang Tang, Yueting Zhuang. 2023-11-21. De-fine: Decomposing and Refining Visual Programs with Auto-Feedback. https://arxiv.org/abs/2311.12890

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