HiMu: Hierarchical Multimodal Frame Selection for Long Video Question Answering

• URL: http://arxiv.org/abs/2603.18558v1
• Date: Thu, 19 Mar 2026 07:11:53 GMT
• Title: HiMu: Hierarchical Multimodal Frame Selection for Long Video Question Answering
• Abstract summary: Long-form video question answering requires reasoning over extended temporal contexts.<n>Existing methods face a sharp trade-off: similarity-based selectors are fast but collapse compositional queries into a single dense vector.<n>We introduce HiMu, a training-free framework that bridges this gap.
• Score: 13.370338205427911
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 introduce HiMu, a training-free framework that bridges this gap. A single text-only LLM call decomposes the query into a hierarchical logic tree whose leaves are atomic predicates, each routed to a lightweight expert spanning vision (CLIP, open-vocabulary detection, OCR) and audio (ASR, CLAP). The resulting signals are normalized, temporally smoothed to align different modalities, and composed bottom-up through fuzzy-logic operators that enforce temporal sequencing and adjacency, producing a continuous satisfaction curve. Evaluations on Video-MME, LongVideoBench and HERBench-Lite show that HiMu advances the efficiency-accuracy Pareto front: at 16 frames with Qwen3-VL 8B it outperforms all competing selectors, and with GPT-4o it surpasses agentic systems operating at 32-512 frames while requiring roughly 10x fewer FLOPs.

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