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 general 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 accurate and robust programs,
setting new benchmarks in the field.

通过引入定义 (De-fine) 的模型无关方法，将复杂任务分解为简单子任务并通过自动反馈优化程序，从而提高逻辑推理性能，在多个视觉任务中实现更准确、更健壮的程序，创造了该领域的新纪录。