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Temporal Saliency Query Network for Efficient Video Recognition

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13694))

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Abstract

Efficient video recognition is a hot-spot research topic with the explosive growth of multimedia data on the Internet and mobile devices. Most existing methods select the salient frames without awareness of the class-specific saliency scores, which neglect the implicit association between the saliency of frames and its belonging category. To alleviate this issue, we devise a novel Temporal Saliency Query (TSQ) mechanism, which introduces class-specific information to provide fine-grained cues for saliency measurement. Specifically, we model the class-specific saliency measuring process as a query-response task. For each category, the common pattern of it is employed as a query and the most salient frames are responded to it. Then, the calculated similarities are adopted as the frame saliency scores. To achieve it, we propose a Temporal Saliency Query Network (TSQNet) that includes two instantiations of the TSQ mechanism based on visual appearance similarities and textual event-object relations. Afterward, cross-modality interactions are imposed to promote the information exchange between them. Finally, we use the class-specific saliencies of the most confident categories generated by two modalities to perform the selection of salient frames. Extensive experiments demonstrate the effectiveness of our method by achieving state-of-the-art results on ActivityNet, FCVID and Mini-Kinetics datasets. Our project page is at https://lawrencexia2008.github.io/projects/tsqnet.

B. Xia and Z. Wang—Co-first authorship.

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Notes

  1. 1.

    Note that the total computation cost of a \(188\times 188\) frame processed by MobileNetv2 and a \(112\times 112\) frame processed by EfficientNet-B0 equals to the cost of a \(224\times 224\) frame processed by MobileNetv2, which is the common setting of previous works [13, 42].

  2. 2.

    Results are obtained on a NVIDIA 3090 GPU with an Intel Xeon E5-2650 v3 @ 2.30 GHz CPU.

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Author information

Authors and Affiliations

  1. Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China

    Boyang Xia & Zhihao Wang

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

    Boyang Xia & Zhihao Wang

  3. The University of Sydney, Sydney, Australia

    Wenhao Wu

  4. Baidu Inc., Beijing, China

    Wenhao Wu & Haoran Wang

  5. Computer Science Department, Aberystwyth University, Aberystwyth, SY23 3FL, UK

    Jungong Han

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  1. Boyang Xia
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  2. Zhihao Wang
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  3. Wenhao Wu
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  4. Haoran Wang
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  5. Jungong Han
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Corresponding author

Correspondence to Wenhao Wu .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Xia, B., Wang, Z., Wu, W., Wang, H., Han, J. (2022). Temporal Saliency Query Network for Efficient Video Recognition. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13694. Springer, Cham. https://doi.org/10.1007/978-3-031-19830-4_42

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