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Does Your Vision-Language Model Get Lost in the Long Video Sampling Dilemma?

Tianyuan Qu, Longxiang Tang,Bohao Peng,Senqiao Yang,Bei Yu,Jiaya Jia

Computing Research Repository (CoRR)(2025)

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Abstract
The rise of Large Vision-Language Models (LVLMs) has significantly advanced video understanding. However, efficiently processing long videos remains a challenge due to the “Sampling Dilemma”: low-density sampling risks missing critical information, while high-density sampling introduces redundancy. To address this issue, we introduce LSDBench, the first benchmark designed to evaluate LVLMs on long-video tasks by constructing high Necessary Sampling Density (NSD) questions, where NSD represents the minimum sampling density required to accurately answer a given question. LSDBench focuses on dense, short-duration actions to rigorously assess the sampling strategies employed by LVLMs. To tackle the challenges posed by high-NSD questions, we propose a novel Reasoning-Driven Hierarchical Sampling (RHS) framework, which combines global localization of question-relevant cues with local dense sampling for precise inference. Additionally, we develop a lightweight Semantic-Guided Frame Selector to prioritize informative frames, enabling RHS to achieve comparable or superior performance with significantly fewer sampled frames. Together, our LSDBench and RHS framework address the unique challenges of high-NSD long-video tasks, setting a new standard for evaluating and improving LVLMs in this domain.
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要点】:本研究针对长视频处理中的“采样困境”,提出了一种新的评估长视频任务的基准LSDBench和一个推理驱动的层次化采样框架RHS,以优化大型视觉语言模型在长视频理解上的性能。

方法】:研究构建了LSDBench,专注于创建具有高必要采样密度(NSD)的问题,并提出了RHS框架,该框架结合了全局相关线索定位和局部密集采样来提高推理精度。

实验】:研究使用LSDBench进行了实验,并通过对比实验证明了RHS框架在采样更少帧的情况下,性能可比较或优于现有方法。具体数据集名称未在摘要中提及。