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ActionFormer: Localizing Moments of Actions with Transformers

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

Self-attention based Transformer models have demonstrated impressive results for image classification and object detection, and more recently for video understanding. Inspired by this success, we investigate the application of Transformer networks for temporal action localization in videos. To this end, we present ActionFormer—a simple yet powerful model to identify actions in time and recognize their categories in a single shot, without using action proposals or relying on pre-defined anchor windows. ActionFormer combines a multiscale feature representation with local self-attention, and uses a light-weighted decoder to classify every moment in time and estimate the corresponding action boundaries. We show that this orchestrated design results in major improvements upon prior works. Without bells and whistles, ActionFormer achieves 71.0% mAP at tIoU = 0.5 on THUMOS14, outperforming the best prior model by 14.1 absolute percentage points. Further, ActionFormer demonstrates strong results on ActivityNet 1.3 (36.6% average mAP) and EPIC-Kitchens 100 (+13.5% average mAP over prior works). Our code is available at https://github.com/happyharrycn/actionformer_release.

C.-L. Zhang—Work was done when visiting UW Madison.

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Notes

  1. 1.

    Without loss of clarity, we drop the index of the pyramid \(\ell \).

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Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Chen-Lin Zhang & Jianxin Wu

  2. 4Paradigm Inc., Beijing, China

    Chen-Lin Zhang

  3. University of Wisconsin-Madison, Madison, USA

    Yin Li

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  1. Chen-Lin Zhang
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  2. Jianxin Wu
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  3. Yin Li
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Correspondence to Chen-Lin Zhang or Yin Li .

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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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Zhang, CL., Wu, J., Li, Y. (2022). ActionFormer: Localizing Moments of Actions with Transformers. 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 13664. Springer, Cham. https://doi.org/10.1007/978-3-031-19772-7_29

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