Reviso: Not All Visual Evidence Deserves Equal Attention in Video Captioning

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.