视频字幕AI的注意力该花在哪，这篇给了答案
现在的视频字幕AI有个毛病：它会把画面里的小细节讲得头头是道，却漏掉真正重要的事。这篇论文的核心观点很直接——不是所有画面信息都值得同等关注，重要性取决于视频内容本身。研究者做了个叫 Reviso 的框架，让AI先快速扫一遍全局，然后自己制定一个观察计划：先判断视频讲的是什么，再列出哪些是关键点，最后决定具体去看哪几段、用什么工具细看。它不再平均用力，而是像人一样带着目的去回看关键片段，看完还能根据新发现调整计划。在四个视频字幕基准上，它比现有开源模型都强，而且生成的字幕能更好地支撑后续的视频问答。这不是你明天能直接用的工具，但它指向一个趋势：AI的注意力正在从“看得全”转向“看得准”。
📄 原文摘要(英文)
Multimodal large language models (MLLMs) can generate detailed video captions, yet they may overemphasize minor details while overlooking essential context and key events. We argue that not all visual evidence deserves equal attention, as the importance of visual information depends on the content of each video. Motivated by this observation, we propose Reviso, an agentic video captioning framework that learns what to observe and how to gather relevant visual evidence. From a low-frame-rate global preview, Reviso constructs a hierarchical Context--Focus--Points (CFP) observation plan that identifies the video context, prioritizes key aspects, and defines concrete observation targets and corresponding tools. Guided by this plan, Reviso performs goal-directed active perception by selectively revisiting temporal segments and combining tools for fine-grained inspection. It interprets the acquired evidence, updates its plan as needed, and integrates global and local information to generate the final caption. To learn this observation strategy, we develop a verification-guided pipeline that constructs high-quality training trajectories through factual verification and teacher-guided refinement. We further train Reviso using Group Relative Policy Optimization (GRPO), incorporating a key-fact reward for caption quality, a Plan Alignment Reward (PAR) for identifying important observation targets, and an Unproductive Tool-Call Penalty (UTCP) that adjusts observation-cost penalties based on caption quality. Experiments across four video captioning benchmarks show that Reviso outperforms the evaluated open-source baselines, while its generated captions better support downstream video question answering. These findings highlight the effectiveness and potential of priority-guided active observation for video captioning.