CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
DTR-GAN: Dilated Temporal Relational Adversarial Network for Video Summarization
Yujia Zhang1,2; Michael Kampffmeyer3; Xiaoguang Zhao1,2; Min Tan1,2
2019-05
Conference NameACM China Turing Award Celebration Conference
Conference Date2019-5
Conference PlaceChengdu, China
Abstract

Video summarization targets the challenge of finding the smallest subset of frames, while still conveying the whole story of a given video. Thus it is of great significance for large-scale video understanding, allowing efficient processing of the large amount of videos that are uploaded every day. In this paper, we introduce a Dilated Temporal Relational Adversarial Network (DTR-GAN) to achieve frame-level video summarization. The dilated temporal relational units in the generator aim to exploit multi-scale temporal context in order to select key frames. To ensure that the model predicts high quality summaries, we present a discriminator that learns to enhance both the information completeness and compactness via a three-player loss. Experiments on the public TVSum dataset demonstrate the effectiveness of the proposed approach.

Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23649
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.Machine Learning Group, UiT The Arctic University of Norway
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,Xiaoguang Zhao,et al. DTR-GAN: Dilated Temporal Relational Adversarial Network for Video Summarization[C],2019.
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