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research-article

DTR-GAN: dilated temporal relational adversarial network for video summarization

Authors:

Michael Kampffmeyer,

Xiaoguang Zhao,

Min TanAuthors Info & Claims

ACM TURC '19: Proceedings of the ACM Turing Celebration Conference - China

Article No.: 89, Pages 1 - 6

https://doi.org/10.1145/3321408.3322622

Published: 17 May 2019 Publication History

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.

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Cited By

Li QZhan ZLi YBhanu B(2025)Spatial–temporal multi-scale interaction for few-shot video summarizationEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109883142(109883)Online publication date: Feb-2025
https://doi.org/10.1016/j.engappai.2024.109883
Huang JGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)Multi-modal Video SummarizationProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3657582(1214-1218)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3652583.3657582
Lin JHua HChen MLi YHsiao JHo CLuo J(2024)VideoXum: Cross-Modal Visual and Textural Summarization of VideosIEEE Transactions on Multimedia10.1109/TMM.2023.333587526(5548-5560)Online publication date: 2024
https://doi.org/10.1109/TMM.2023.3335875
Show More Cited By

Index Terms

DTR-GAN: dilated temporal relational adversarial network for video summarization
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Video summarization

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cover image ACM Other conferences

ACM TURC '19: Proceedings of the ACM Turing Celebration Conference - China

May 2019

963 pages

ISBN:9781450371582

DOI:10.1145/3321408

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 May 2019

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Norwegian Research Council FRIPRO
National Natural Science Foundation of China

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ACM TURC 2019

ACM TURC 2019: ACM Turing Celebration Conference - China

May 17 - 19, 2019

Chengdu, China

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Cited By

Li QZhan ZLi YBhanu B(2025)Spatial–temporal multi-scale interaction for few-shot video summarizationEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109883142(109883)Online publication date: Feb-2025
https://doi.org/10.1016/j.engappai.2024.109883
Huang JGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)Multi-modal Video SummarizationProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3657582(1214-1218)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3652583.3657582
Lin JHua HChen MLi YHsiao JHo CLuo J(2024)VideoXum: Cross-Modal Visual and Textural Summarization of VideosIEEE Transactions on Multimedia10.1109/TMM.2023.333587526(5548-5560)Online publication date: 2024
https://doi.org/10.1109/TMM.2023.3335875
Yu QJin HSun YYu H(2024)SUM-GAN-FGN: Frequency Domain Unsupervised Video Summarization with Gaussian Noise Based on GAN2024 International Conference on Cyber-Physical Social Intelligence (ICCSI)10.1109/ICCSI62669.2024.10799434(1-6)Online publication date: 8-Nov-2024
https://doi.org/10.1109/ICCSI62669.2024.10799434
Singh ASrivastava DTapaswi M(2024)“Previously on…” from Recaps to Story Summarization2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.01294(13635-13646)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.01294
Krishnan HGladwin JNambiar NSamanuai AVyshakh Madhu T(2024)Video summarization using transformer model assisted AC-SUM-GAN2023 4th International Conference on Intelligent Technologies (CONIT)10.1109/CONIT61985.2024.10627023(1-8)Online publication date: 21-Jun-2024
https://doi.org/10.1109/CONIT61985.2024.10627023
El-Nagar GEl-Sawy ARashad M(2024)A deep audio-visual model for efficient dynamic video summarizationJournal of Visual Communication and Image Representation10.1016/j.jvcir.2024.104130100(104130)Online publication date: Apr-2024
https://doi.org/10.1016/j.jvcir.2024.104130
Yoon UHong MJo G(2023)Unsupervised Video Summarization Based on Deep Reinforcement Learning with InterpolationSensors10.3390/s2307338423:7(3384)Online publication date: 23-Mar-2023
https://doi.org/10.3390/s23073384
Khurana KDeshpande U(2023)Two stream multi-layer convolutional network for keyframe-based video summarizationMultimedia Tools and Applications10.1007/s11042-023-14665-x82:25(38467-38508)Online publication date: 16-Mar-2023
https://doi.org/10.1007/s11042-023-14665-x
Saini PKumar KKashid SSaini ANegi A(2023)Video summarization using deep learning techniques: a detailed analysis and investigationArtificial Intelligence Review10.1007/s10462-023-10444-056:11(12347-12385)Online publication date: 15-Mar-2023
https://doi.org/10.1007/s10462-023-10444-0
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