Mixture of Rationale: Multi-Modal Reasoning Mixture for Visual Question Answering
This addresses the problem of low diversity and poor modality alignment in multi-modal reasoning for visual question answering, representing an incremental advancement.
The paper tackles the challenge of zero-shot visual question answering by proposing Mixture of Rationales (MoR), a method that mixes multiple rationales to improve reasoning across modalities, achieving a 12.43% accuracy improvement on NLVR2 and a 2.45% improvement on OKVQA-S.
Zero-shot visual question answering (VQA) is a challenging task that requires reasoning across modalities. While some existing methods rely on a single rationale within the Chain of Thoughts (CoT) framework, they may fall short of capturing the complexity of the VQA problem. On the other hand, some other methods that use multiple rationales may still suffer from low diversity, poor modality alignment, and inefficient retrieval and fusion. In response to these challenges, we propose \emph{Mixture of Rationales (MoR)}, a novel multi-modal reasoning method that mixes multiple rationales for VQA. MoR uses a single frozen Vision-and-Language Pre-trained Models (VLPM) model to {dynamically generate, retrieve and fuse multi-modal thoughts}. We evaluate MoR on two challenging VQA datasets, i.e. NLVR2 and OKVQA, with two representative backbones OFA and VL-T5. MoR achieves a 12.43\% accuracy improvement on NLVR2, and a 2.45\% accuracy improvement on OKVQA-S( the science and technology category of OKVQA).