CREST selects video frames using curvature-adaptive non-maximum suppression on CLIP relevance scores, beating AKS by ~0.5% on two long-video QA benchmarks while using a fraction of MIRA's preprocessing cost.
PLLaV A (Xu et al., 2024b) improves parameter efficiency, yet its performance is directly bounded by the quality of the frames it receives
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CREST: Curvature-Regulated Event-Centric Sampling for Efficient Long-Video Understanding
CREST selects video frames using curvature-adaptive non-maximum suppression on CLIP relevance scores, beating AKS by ~0.5% on two long-video QA benchmarks while using a fraction of MIRA's preprocessing cost.