HiVU

Paper: AdaVideoRAG: Omni-Contextual Adaptive Retrieval-Augmented Efficient Long Video Understanding Code: xzc-zju/AdaVideoRAG Background Long-video understanding is hard because the useful evidence is sparse, long-range, and often spread across multiple modalities: visual content; speech; scene text; temporal relations. RAG is a natural fit here. Instead of feeding the whole video to the MLLM every time, the system can first build a searchable memory, retrieve relevant evidence, and then answer with a smaller context. But a fixed VideoRAG pipeline is not ideal. Easy questions may not need retrieval at all, while hard questions may need structured graph reasoning. ...

This post is a small index for the benchmarks that appear repeatedly in recent streaming video / long-video VLM papers. The main split is simple: online streaming benchmarks test whether the model can answer while the video is still coming in; offline long-video benchmarks test long-context video understanding, but usually assume the whole video is already available; standard video QA benchmarks are useful for comparability, but they are not the real target of streaming-memory papers. The tables below are copied or compacted from the corresponding method papers. They should not be read as one unified leaderboard: backbones, frame rates, memory budgets, judge versions, subtitle settings, and dataset splits often differ. ...