1. Query-Biased Self-Attentive Network for Query-Focused Video Summarization.
- Author
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Xiao, Shuwen, Zhao, Zhou, Zhang, Zijian, Guan, Ziyu, and Cai, Deng
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VIDEO summarization - Abstract
This paper addresses the task of query-focused video summarization, which takes user queries and long videos as inputs and generates query-focused video summaries. Compared to video summarization, which mainly concentrates on finding the most diverse and representative visual contents as a summary, the task of query-focused video summarization considers the user’s intent and the semantic meaning of generated summary. In this paper, we propose a method, named query-biased self-attentive network (QSAN) to tackle this challenge. Our key idea is to utilize the semantic information from video descriptions to generate a generic summary and then to combine the information from the query to generate a query-focused summary. Specifically, we first propose a hierarchical self-attentive network to model the relative relationship at three levels, which are different frames from a segment, different segments of the same video, textual information of video description and its related visual contents. We train the model on video caption dataset and employ a reinforced caption generator to generate a video description, which can help us locate important frames or shots. Then we build a query-aware scoring module to compute the query-relevant score for each shot and generate the query-focused summary. Extensive experiments on the benchmark dataset demonstrate the competitive performance of our approach compared to some methods. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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