Video Retrieval

Video retrieval studies how to find the videos or time spans that best match a query, supporting search, question answering, and evidence gathering. Common approaches use contrastive dual-encoder embeddings for fast candidate search, then apply query expansion, multimodal reranking, or temporal localization to refine results; long-video systems also use keyframe indexes, memory, and retrieval-augmented generation. Benchmarks such as MSR-VTT and ActivityNet test text-to-video matching and moment localization, while long-video and domain-specific evaluations probe extended evidence, actions, and specialized content. A central tension is balancing scalable semantic matching with precise temporal and contextual reasoning: systems can find broadly relevant clips yet miss action order, fine-grained details, or queries with no valid match, while deeper verification costs more.