Knowledge Commons of Institute of Automation,CAS
Unsupervised Video Summarization via Relation-Aware Assignment Learning | |
Gao, Junyu1,2,3; Yang, Xiaoshan1,2,3; Zhang, Yingying1,2,3; Xu, Changsheng1,2,3 | |
发表期刊 | IEEE TRANSACTIONS ON MULTIMEDIA |
ISSN | 1520-9210 |
2021 | |
卷号 | 23页码:3203-3214 |
摘要 | We address the problem of unsupervised video summarization that automatically selects key video clips. Most state-of-the-art approaches suffer from two issues: (1) they model video clips without explicitly exploiting their relations, and (2) they learn soft importance scores over all the video clips to generate the summary representation. However, a meaningful video summary should be inferred by taking the relation-aware context of the original video into consideration, and directly selecting a subset of clips with a hard assignment. In this paper, we propose to exploit clip-clip relations to learn relation-aware hard assignments for selecting key clips in an unsupervised manner. First, we consider the clips as graph nodes to construct an assignment-learning graph. Then, we utilize the magnitude of the node features to generate hard assignments as the summary selection. Finally, we optimize the whole framework via a proposed multi-task loss including a reconstruction constraint, and a contrastive constraint. Extensive experimental results on three popular benchmarks demonstrate the favourable performance of our approach. |
关键词 | Feature extraction Training Optimization Semantics Recurrent neural networks Task analysis Graph neural network unsupervised learning video summarization |
DOI | 10.1109/TMM.2020.3021980 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2018AAA0102200] ; National Natural Science Foundation of China[61720106006] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[61832002] ; National Natural Science Foundation of China[61702511] ; National Natural Science Foundation of China[61751211] ; National Natural Science Foundation of China[61532009] ; National Natural Science Foundation of China[U1836220] ; National Natural Science Foundation of China[U1705262] ; National Natural Science Foundation of China[61872424] ; National Natural Science Foundation of China[61936005] ; Key Research Program of Frontier Sciences of CAS[QYZDJSSWJSC039] ; Research Program of National Laboratory of Pattern Recognition[Z-2018007] |
项目资助者 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Key Research Program of Frontier Sciences of CAS ; Research Program of National Laboratory of Pattern Recognition |
WOS研究方向 | Computer Science ; Telecommunications |
WOS类目 | Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications |
WOS记录号 | WOS:000698902000020 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 图像视频处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/45733 |
专题 | 多模态人工智能系统全国重点实验室_多媒体计算 |
通讯作者 | Xu, Changsheng |
作者单位 | 1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.PengCheng Lab, Shenzhen 518066, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Gao, Junyu,Yang, Xiaoshan,Zhang, Yingying,et al. Unsupervised Video Summarization via Relation-Aware Assignment Learning[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2021,23:3203-3214. |
APA | Gao, Junyu,Yang, Xiaoshan,Zhang, Yingying,&Xu, Changsheng.(2021).Unsupervised Video Summarization via Relation-Aware Assignment Learning.IEEE TRANSACTIONS ON MULTIMEDIA,23,3203-3214. |
MLA | Gao, Junyu,et al."Unsupervised Video Summarization via Relation-Aware Assignment Learning".IEEE TRANSACTIONS ON MULTIMEDIA 23(2021):3203-3214. |
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