Recent advances in visual reasoning (VR), particularly with the aid of Large
Vision-Language Models (VLMs), show promise but require access to large-scale
datasets and face challenges such as high computational costs and limited
generalization capabilities. Compositional visual reasoning approaches have
emerged as effective strategies; however, they heavily rely on the commonsense
knowledge encoded in Large Language Models (LLMs) to perform planning,
reasoning, or both, without considering the effect of their decisions on the
visual reasoning process, which can lead to errors or failed procedures. To
address these challenges, we introduce HYDRA, a multi-stage dynamic
compositional visual reasoning framework designed for reliable and
incrementally progressive general reasoning. HYDRA integrates three essential
modules: a planner, a Reinforcement Learning (RL) agent serving as a cognitive
controller, and a reasoner. The planner and reasoner modules utilize an LLM to
generate instruction samples and executable code from the selected instruction,
respectively, while the RL agent dynamically interacts with these modules,
making high-level decisions on selection of the best instruction sample given
information from the historical state stored through a feedback loop. This
adaptable design enables HYDRA to adjust its actions based on previous feedback
received during the reasoning process, leading to more reliable reasoning
outputs and ultimately enhancing its overall effectiveness. Our framework
demonstrates state-of-the-art performance in various VR tasks on four different
widely-used datasets.

HYDRA 是一个多阶段动态组合视觉推理框架，利用大规模数据集和语言模型进行可靠且递增进化的推理，表现出了在各种视觉推理任务上的最新性能。