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

Paper recorded by Signals 4 on 2026-11-02 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-11-02 on arXiv · recorded by Signals 4 on 2026-11-03

Category: cs.CV · 计算机视觉 · first seen 2026-11-03

Abstract

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 framewor

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#21 most recent of 710 cs.CV papers we have recorded · ↑ newer: Isekai3D SDG: A Platform for Diverse Synthetic Gameplay Visual Data · ↓ older: 3D-BBDA: Backpropagation-Free Black-Box Domain Adaptation for Robust 3
Cite this page: Reviso: Not All Visual Evidence Deserves Equal Attention in Video Captioning: the #21 most recent of 710 cs.CV papers we have recorded (as of 2026-11-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/reviso-not-all-visual-evidence-deserves-equal-attention-in-video-captioning.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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