新框架通过VLM引导的过渡发现改进视频字幕生成

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-04 04:00

研究人员开发了一个名为Seeing Before Synthesizing (SBS)的新框架，以改进弱监督密集视频字幕生成。该方法使用视觉语言模型（VLMs）为事件之间的视频片段生成帧级叙述，并根据语义变化识别过渡。然后，该框架通过融合时间中点和语义变化点来优化视觉语言对齐，从而精炼这些过渡的时间掩码。在ActivityNet Captions和YouCook2数据集上的实验表明，SBS在视频事件定位和字幕生成方面均达到了最先进的性能。 AI

影响通过在弱监督设置下提高事件定位和描述的准确性，增强了视频理解能力。

排序理由详细介绍视频字幕新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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Newsworthiness bucket: Tool; 详细介绍视频字幕新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Ye-Chan Kim, Seunghee Choi, SeungJu Cha, Si-Woo Kim, Hwiseon Kim, Hyungee Kim, Dong-Jin Kim · 2026-09-04 04:00

先看后合：VLM引导的弱监督密集视频字幕生成过渡事件发现

arXiv:2609.04183v1 Announce Type: cross Abstract: Weakly-Supervised Dense Video Captioning aims to localize and describe multiple events in untrimmed videos given only an ordered set of event-level captions per video. Recent work synthesizes auxiliary transition captions via LLM …

本文如何被排名

报道来源 [1]

先看后合：VLM引导的弱监督密集视频字幕生成过渡事件发现

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