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CoTeRe-Net: Discovering Collaborative Ternary Relations in Videos

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12351))

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Abstract

Modeling relations is crucial to understand videos for action and behavior recognition. Current relation models mainly reason about relations of invisibly implicit cues, while important relations of visually explicit cues are rarely considered, and the collaboration between them is usually ignored. In this paper, we propose a novel relation model that discovers relations of both implicit and explicit cues as well as their collaboration in videos. Our model concerns Collaborative Ternary Relations (CoTeRe), where the ternary relation involves channel (C, for implicit), temporal (T, for implicit), and spatial (S, for explicit) relation (R). We devise a flexible and effective CTSR module to collaborate ternary relations for 3D-CNNs, and then construct CoTeRe-Nets for action recognition. Extensive experiments on both ablation study and performance evaluation demonstrate that our CTSR module is significantly effective with approximate \(3\%\) gains and our CoTeRe-Nets outperform state-of-the-art approaches on three popular benchmarks. Boosts analysis and relations visualization also validate that relations of both implicit and explicit cues are discovered with efficacy by our method. Our code is available at https://github.com/zhenglab/cotere-net.

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Acknowledgment

This work was supported by the National Natural Science Foundation of China under grant numbers 61771440 and 41776113.

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Authors and Affiliations

  1. Underwater Vision Lab, Ocean University of China, Qingdao, China

    Zhensheng Shi, Cheng Guan, Liangjie Cao, Qianqian Li, Ju Liang, Zhaorui Gu, Haiyong Zheng & Bing Zheng

  2. Sanya Oceanographic Institution, Ocean University of China, Sanya, China

    Bing Zheng

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  1. Zhensheng Shi
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  2. Cheng Guan
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  3. Liangjie Cao
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Correspondence to Haiyong Zheng or Bing Zheng .

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  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Shi, Z. et al. (2020). CoTeRe-Net: Discovering Collaborative Ternary Relations in Videos. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12351. Springer, Cham. https://doi.org/10.1007/978-3-030-58539-6_23

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  • DOI: https://doi.org/10.1007/978-3-030-58539-6_23

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