https://scholars.lib.ntu.edu.tw/handle/123456789/581252
標題: | Transforming Multi-concept Attention into Video Summarization | 作者: | Liu Y.-T Li Y.-J Wang Y.-C.F. YU-CHIANG WANG |
關鍵字: | Video recording; Attention mechanisms; Complete experiment; Labeled and unlabeled data; Real-world; State of the art; Video data; Video features; Video summarization; Computer vision | 公開日期: | 2021 | 卷: | 12626 LNCS | 起(迄)頁: | 498-513 | 來源出版物: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 摘要: | Video summarization is among the challenging tasks in computer vision, which aims at identifying highlight frames or shots over a lengthy video input. In this paper, we propose a novel attention-based framework for video summarization with complex video data. Unlike previous works which only apply attention mechanism on the correspondence between frames, our multi-concept video self-attention (MC-VSA) model is presented to identify informative regions across temporal and concept video features, which jointly exploit context diversity over time and space for summarization purposes. Together with consistency between video and summary enforced in our framework, our model can be applied to both labeled and unlabeled data, making our method preferable to real-world applications. Extensive and complete experiments on two benchmarks demonstrate the effectiveness of our model both quantitatively and qualitatively, and confirms its superiority over the state-of-the-arts. ? 2021, Springer Nature Switzerland AG. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103354742&doi=10.1007%2f978-3-030-69541-5_30&partnerID=40&md5=3163aa2413687e12b2937bb592e3eb43 https://scholars.lib.ntu.edu.tw/handle/123456789/581252 |
ISSN: | 03029743 | DOI: | 10.1007/978-3-030-69541-5_30 |
顯示於: | 電機工程學系 |
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