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Dilated temporal relational adversarial network for generic video summarization

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Abstract

The large amount of videos popping up every day, make it more and more critical that key information within videos can be extracted and understood in a very short time. Video summarization, the task of finding the smallest subset of frames, which still conveys the whole story of a given video, is thus of great significance to improve efficiency of video understanding. We propose a novel Dilated Temporal Relational Generative Adversarial Network (DTR-GAN) to achieve frame-level video summarization. Given a video, it selects the set of key frames, which contain the most meaningful and compact information. Specifically, DTR-GAN learns a dilated temporal relational generator and a discriminator with three-player loss in an adversarial manner. A new dilated temporal relation (DTR) unit is introduced to enhance temporal representation capturing. The generator uses this unit to effectively exploit global multi-scale temporal context to select key frames and to complement the commonly used Bi-LSTM. To ensure that summaries capture enough key video representation from a global perspective rather than a trivial randomly shorten sequence, we present a discriminator that learns to enforce both the information completeness and compactness of summaries via a three-player loss. The loss includes the generated summary loss, the random summary loss, and the real summary (ground-truth) loss, which play important roles for better regularizing the learned model to obtain useful summaries. Comprehensive experiments on three public datasets show the effectiveness of the proposed approach.

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Acknowledgements

We would like to thank Xiaohui Zeng for her valuable discussions. This project is supported by the Department of Defense under Contract No. FA8702-15-D-0002 with Carnegie Mellon University for the operation of the Software Engineering Institute, a federally funded research and development center. This work is also partially funded by the National Natural Science Foundation of China (Grant No. 61673378 and 61333016), and Norwegian Research Council FRIPRO grant no. 239844 on developing the Next Generation Learning Machines.

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Authors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China

    Yujia Zhang & Min Tan

  2. University of Chinese Academy of Sciences, 100049, Beijing, China

    Yujia Zhang & Min Tan

  3. Machine Learning Group, UiT The Arctic University of Norway, 9019, Tromsø, Norway

    Michael Kampffmeyer

  4. Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Xiaodan Liang & Eric P. Xing

  5. Xidian University, 710071, Xi’an, China

    Dingwen Zhang

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  1. Yujia Zhang
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Correspondence to Yujia Zhang.

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Zhang, Y., Kampffmeyer, M., Liang, X. et al. Dilated temporal relational adversarial network for generic video summarization. Multimed Tools Appl 78, 35237–35261 (2019). https://doi.org/10.1007/s11042-019-08175-y

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