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, ActivityNet-QA) with state-of-the-art results, and extensions to related tasks (grounded videoQA, paragraph captioning).

通过分解为多阶段的模块化推理框架来解决视频问答（videoQA）任务，我们的方法MoReVQA通过事件解析器、定位阶段、最终推理阶段和外部存储器在标准视频问答基准测试中取得了最先进的结果，并在相关任务（基于视频的问答、段落字幕生成）上得到扩展。

MoReVQA: 探索视频问答的模块化推理模型