Video Question Answering

Video question answering asks models to identify events, people, actions, and relationships in video, including when things happen and why. Approaches include vision-language models, question-aware frame selection and retrieval, temporal memory, scene graphs, and agentic systems that inspect evidence with tools. Benchmarks test settings such as long-form and streaming video, egocentric activity, causal reasoning, and precise temporal grounding. A central challenge is that high answer accuracy can coexist with weak use of visual evidence, poor temporal grounding, and overconfidence when evidence is incomplete.