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
Reviso is an agentic video captioning framework that learns to prioritize key visual evidence through hierarchical planning and active perception, improving caption quality and downstream video understanding.
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…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
Reviso: Not All Visual Evidence Deserves Equal Attention in Video Captioning was first seen on Hugging Face · Daily papers on Nov 3, 2026. Since then it has appeared in 11 checks on 2 different days. Its best position was #6 of 30, reached on Nov 4, 05:28 UTC. It dropped off the list after Nov 4, 05:28 UTC.
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#6 on Hugging Face · Daily papers, on Nov 4, 05:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 3, 2026 and has been on the list on 2 days (11 checks) since.