Towards Top-Down Reasoning: An Explainable Multi-Agent Approach for Visual Question Answering

Recently, Vision Language Models (VLMs) have gained significant attention, exhibiting notable advancements across various tasks by leveraging extensive image-text paired data. However, prevailing VLMs often treat Visual Question Answering (VQA) as perception tasks, employing black-box models that overlook explicit modeling of relationships between different questions within the same visual scene. Moreover, the existing VQA methods that rely on Knowledge Bases (KBs) might frequently encounter biases from limited data and face challenges in relevant information indexing. Attempt to overcome these limitations, this paper introduces an explainable multi-agent collaboration framework by tapping into knowledge embedded in Large Language Models (LLMs) trained on extensive corpora. Inspired by human cognition, our framework uncovers latent information within the given question by employing three agents, i.e., Seeker, Responder, and Integrator, to perform a top-down reasoning process. The Seeker agent generates relevant issues related to the original question. The Responder agent, based on VLM, handles simple VQA tasks and provides candidate answers. The Integrator agent combines information from the Seeker agent and the Responder agent to produce the final VQA answer. Through the above collaboration mechanism, our framework explicitly constructs a multi-view knowledge base for a specific image scene, reasoning answers in a top-down processing manner. We extensively evaluate our method on diverse VQA datasets and VLMs, demonstrating its broad applicability and interpretability with comprehensive experimental results.

VisualProg Distiller: Learning to Fine-tune Non-differentiable Visual Programming Frameworks

As an interpretable and universal neuro-symbolic paradigm based on Large Language Models, visual programming (VisualProg) can execute compositional visual tasks without training, but its performance is markedly inferior compared to task-specific supervised learning models. To increase its practicality, the performance of VisualProg on specific tasks needs to be improved. However, the non-differentiability of VisualProg limits the possibility of employing the fine-tuning strategy on specific tasks to achieve further improvements. In our analysis, we discovered that significant performance issues in VisualProg's execution originated from errors made by the sub-modules at corresponding visual sub-task steps. To address this, we propose ``VisualProg Distiller", a method of supplementing and distilling process knowledge to optimize the performance of each VisualProg sub-module on decoupled visual sub-tasks, thus enhancing the overall task performance. Specifically, we choose an end-to-end model that is well-performed on the given task as the teacher and further distill the knowledge of the teacher into the invoked visual sub-modules step-by-step based on the execution flow of the VisualProg-generated programs. In this way, our method is capable of facilitating the fine-tuning of the non-differentiable VisualProg frameworks effectively. Extensive and comprehensive experimental evaluations demonstrate that our method can achieve a substantial performance improvement of VisualProg, and outperforms all the compared state-of-the-art methods by large margins. Furthermore, to provide valuable process supervision for the GQA task, we construct a large-scale dataset by utilizing the distillation process of our method.