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MotionSqueeze: Neural Motion Feature Learning for Video Understanding

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12361))

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Abstract

Motion plays a crucial role in understanding videos and most state-of-the-art neural models for video classification incorporate motion information typically using optical flows extracted by a separate off-the-shelf method. As the frame-by-frame optical flows require heavy computation, incorporating motion information has remained a major computational bottleneck for video understanding. In this work, we replace external and heavy computation of optical flows with internal and light-weight learning of motion features. We propose a trainable neural module, dubbed MotionSqueeze, for effective motion feature extraction. Inserted in the middle of any neural network, it learns to establish correspondences across frames and convert them into motion features, which are readily fed to the next downstream layer for better prediction. We demonstrate that the proposed method provides a significant gain on four standard benchmarks for action recognition with only a small amount of additional cost, outperforming the state of the art on Something-Something-V1 & V2 datasets.

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Acknowledgements

This work is supported by Samsung Advanced Institute of Technology (SAIT), and also by Basic Science Research Program (NRF-2017R1E1A1A010 77999, NRF-2018R1C1B6001223) and Next-Generation Information Computing Development Program (NRF-2017M3C4A7069369) through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT.

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Authors and Affiliations

  1. POSTECH, Pohang University of Science and Technology, Pohang, Korea

    Heeseung Kwon, Manjin Kim, Suha Kwak & Minsu Cho

  2. NPRC, The Neural Processing Research Center, Seoul, Korea

    Heeseung Kwon & Minsu Cho

Authors
  1. Heeseung Kwon
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  2. Manjin Kim
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  3. Suha Kwak
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  4. Minsu Cho
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Corresponding author

Correspondence to Minsu Cho .

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Editors and Affiliations

  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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Kwon, H., Kim, M., Kwak, S., Cho, M. (2020). MotionSqueeze: Neural Motion Feature Learning for Video Understanding. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12361. Springer, Cham. https://doi.org/10.1007/978-3-030-58517-4_21

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  • DOI: https://doi.org/10.1007/978-3-030-58517-4_21

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-58517-4

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