CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
Query-Conditioned Three-Player Adversarial Network for Video Summarization
Yujia Zhang1,2; Michael Kampffmeyer3; Xiaodan Liang4; Min Tan1,2; Eric P. Xing4
2018-08
Conference NameBritish Machine Vision Conference
Conference Date2018-8
Conference PlaceNewcastle upon Tyne, UK
Abstract

Video summarization plays an important role in video understanding by selecting key frames/shots. Traditionally, it aims to find the most representative and diverse contents in a video as short summaries. Recently, a more generalized task, query-conditioned video summarization, has been introduced, which takes user queries into consideration to learn more user-oriented summaries. In this paper, we propose a query-conditioned three-player generative adversarial network to tackle this challenge. The generator learns the joint representation of the user query and the video content, and the discriminator takes three pairs of query-conditioned summaries as the input to discriminate the real summary from a generated and a random one. A three-player loss is introduced for joint training of the generator and the discriminator, which forces the generator to learn better summary results, and avoids the generation of random trivial summaries. Experiments on a recently proposed query-conditioned video summarization benchmark dataset show the efficiency and efficacy of our proposed method.

Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23650
Collection复杂系统管理与控制国家重点实验室_先进机器人
Corresponding AuthorYujia Zhang
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.UiT The Arctic University of Norway
4.Carnegie Mellon University
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yujia Zhang,Michael Kampffmeyer,Xiaodan Liang,et al. Query-Conditioned Three-Player Adversarial Network for Video Summarization[C],2018.
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