AI RESEARCH
HiMu: Hierarchical Multimodal Frame Selection for Long Video Question Answering
arXiv CS.AI
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ArXi:2603.18558v1 Announce Type: cross Long-form video question answering requires reasoning over extended temporal contexts, making frame selection critical for large vision-language models (LVLMs) bound by finite context windows. Existing methods face a sharp trade-off: similarity-based selectors are fast but collapse compositional queries into a single dense vector, losing sub-event ordering and cross-modal bindings; agent-based methods recover this structure through iterative LVLM inference, but at prohibitive cost. We.