Agent-based Video Trimming

• URL: http://arxiv.org/abs/2412.09513v1
• Date: Thu, 12 Dec 2024 17:59:28 GMT
• Title: Agent-based Video Trimming
• Authors: Lingfeng Yang, Zhenyuan Chen, Xiang Li, Peiyang Jia, Liangqu Long, Jian Yang,
• Abstract summary: We introduce a novel task called Video Trimming (VT) VT focuses on detecting wasted footage, selecting valuable segments, and composing them into a final video with a coherent story. AVT received more favorable evaluations in user studies and demonstrated superior mAP and precision on the YouTube Highlights, TVSum, and our own dataset for the highlight detection task.
• Score: 17.519404251018308
• License:
• Abstract: As information becomes more accessible, user-generated videos are increasing in length, placing a burden on viewers to sift through vast content for valuable insights. This trend underscores the need for an algorithm to extract key video information efficiently. Despite significant advancements in highlight detection, moment retrieval, and video summarization, current approaches primarily focus on selecting specific time intervals, often overlooking the relevance between segments and the potential for segment arranging. In this paper, we introduce a novel task called Video Trimming (VT), which focuses on detecting wasted footage, selecting valuable segments, and composing them into a final video with a coherent story. To address this task, we propose Agent-based Video Trimming (AVT), structured into three phases: Video Structuring, Clip Filtering, and Story Composition. Specifically, we employ a Video Captioning Agent to convert video slices into structured textual descriptions, a Filtering Module to dynamically discard low-quality footage based on the structured information of each clip, and a Video Arrangement Agent to select and compile valid clips into a coherent final narrative. For evaluation, we develop a Video Evaluation Agent to assess trimmed videos, conducting assessments in parallel with human evaluations. Additionally, we curate a new benchmark dataset for video trimming using raw user videos from the internet. As a result, AVT received more favorable evaluations in user studies and demonstrated superior mAP and precision on the YouTube Highlights, TVSum, and our own dataset for the highlight detection task. The code and models are available at https://ylingfeng.github.io/AVT.

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