arXiv Computer Vision
Beyond Frame Selection: Rethinking Long-Video Understanding with MLLMs
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Beyond Frame Selection: Rethinking Long-Video Understanding with MLLMs
Multimodal Large Language Models (MLLMs) have achieved strong progress in video understanding, yet it remains challenging because the token limitation makes MLLMs difficult to capture temporally sparse evidence. Existing methods typically rely on uniform sampling, or frame selection, but these strategies usually optimize either broad temporal coverage or local relevance, making it difficult to preserve both global storyline context and fine-grained evidence.
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