会议专题

DTR-GAN:Dilated Temporal Relational Adversarial Network for Video Summarization

  Video summarization targets the challenge of finding the small-est 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 pre-dicts 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.

video summarization dilated temporal relation generative adver-sarial network three-player loss

Yujia Zhang Michael Kampffmeyer Xiaoguang Zhao Min Tan

Institute of Automation,Chinese Academy of Sciences University of Chinese Academy of Sciences Machine Learning Group UiT The Arctic University of Norway

国际会议

2019国图灵大会(ACM Turing Celebration conference-China 2019 )

成都

英文

157-162

2019-05-17(万方平台首次上网日期,不代表论文的发表时间)