看到场景才重要：通过场景感知的长视频基准揭示视频理解模型中的遗忘现象
Seeing the Scene Matters: Revealing Forgetting in Video Understanding Models with a Scene-Aware Long-Video Benchmark
AI总结：
本文提出SceneBench基准，揭示视频理解模型在长场景上下文中的遗忘问题，并提出Scene-RAG方法提升性能2.50%。
摘要：
长视频理解（LVU）仍然是多模态学习中的核心挑战。尽管最近的视觉-语言模型（VLMs）取得了显著进展，但现有基准主要关注细粒度感知或粗粒度总结，对长上下文的时序理解提供有限见解。本文定义场景为视频中视觉和语义上下文保持一致的连贯片段，由此提出关键问题：当前VLMs能否有效推理长场景上下文？为此，我们引入SceneBench基准，提供场景级挑战。评估发现，当VLMs尝试回答场景级问题时，准确率显著下降，表明存在长范围上下文遗忘。为进一步验证，我们提出Scene Retrieval-Augmented Generation（Scene-RAG），通过跨场景检索和整合相关上下文构建动态场景记忆。Scene-RAG将VLM性能提升2.50%，证实当前模型仍难以处理长上下文保留。我们希望SceneBench能推动未来研究向更稳健、类人化的视频理解VLMs发展。
英文摘要：
Long video understanding (LVU) remains a core challenge in multimodal learning. Although recent vision-language models (VLMs) have made notable progress, existing benchmarks mainly focus on either fine-grained perception or coarse summarization, offering limited insight into temporal understanding over long contexts. In this work, we define a scene as a coherent segment of a video in which both visual and semantic contexts remain consistent, aligning with human perception. This leads us to a key question: can current VLMs reason effectively over long, scene-level contexts? To answer this, we introduce a new benchmark, SceneBench, designed to provide scene-level challenges. Our evaluation reveals a sharp drop in accuracy when VLMs attempt to answer scene-level questions, indicating significant forgetting of long-range context. To further validate these findings, we propose Scene Retrieval-Augmented Generation (Scene-RAG), which constructs a dynamic scene memory by retrieving and integrating relevant context across scenes. This Scene-RAG improves VLM performance by +2.50%, confirming that current models still struggle with long-context retention. We hope SceneBench will encourage future research toward VLMs with more robust, human-like video comprehension.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
