MoReVQA: Exploring Modular Reasoning Models for Video Question Answering

Juhong Min, Shyamal Buch, Arsha Nagrani, Minsu Cho, Cordelia Schmid; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 13235-13245

This paper addresses the task of video question answering (videoQA) via a decomposed multi-stage modular reasoning framework. Previous modular methods have shown promise with a single planning stage ungrounded in visual content. However through a simple and effective baseline we find that such systems can lead to brittle behavior in practice for challenging videoQA settings. Thus unlike traditional single-stage planning methods we propose a multi-stage system consisting of an event parser a grounding stage and a final reasoning stage in conjunction with an external memory. All stages are training-free and performed using few-shot prompting of large models creating interpretable intermediate outputs at each stage. By decomposing the underlying planning and task complexity our method MoReVQA improves over prior work on standard videoQA benchmarks (NExT-QA iVQA EgoSchema and ActivityNet-QA) with state-of-the-art results and extensions to related tasks (grounded videoQA paragraph captioning).