AI RESEARCH
VideoITG: Multimodal Video Understanding with Instructed Temporal Grounding
arXiv CS.AI
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ArXi:2507.13353v2 Announce Type: replace-cross While Video Large Language Models (Video-LLMs) have shown significant potential in multimodal understanding and reasoning tasks, how to efficiently select the most informative frames from videos remains a critical challenge. Existing methods attempt to optimize frame sampling by reducing inter-frame redundancy or employing unsupervised event localization.