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Multi-scale Motion-Aware Module for Video Action Recognition

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Computer Vision – ECCV 2022 Workshops (ECCV 2022)

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Abstract

Due to the lengthy computing time for optical flow, recent works have proposed to use the correlation operation as an alternative approach to extracting motion features. Although using correlation operations shows significant improvement with negligible FLOPs, it introduces much more latency per FLOP than convolution operations and increases noticeable latency as a larger searching patch is applied. Nonetheless, shrinking the searching patch in correlation operation is doomed to degrade its performance owing to the inability to capture larger displacements. In this paper, we propose an effective and low-latency Multi-Scale Motion-Aware (MSMA) module. It uses smaller searching patches at different scales for efficiently extracting motion features from large displacements. It can be installed into and generalizes well on different CNN backbones. When installed into TSM ResNet-50, the MSMA module introduces \(\approx \) 17.6% more latency on NVIDIA Tesla V100 GPU, yet, it achieves state-of-the-art performance on Something-Something V1 & V2 and Diving-48.

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Acknowledgments

This research is co-sponsored by ITRI and Ministry of Science and Technology (MoST). This work is also financially supported by “Center for Open Intelligent Connectivity” of “Higher Education Sprout Project” of NYCU and MOE, Taiwan.

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  1. Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan, Republic of China

    Huai-Wei Peng & Yu-Chee Tseng

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  1. Huai-Wei Peng
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Correspondence to Huai-Wei Peng .

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  1. IBM Research - MIT-IBM Watson AI Lab, Massachusetts, USA

    Leonid Karlinsky

  2. Technion – Israel Institute of Technology, Haifa, Israel

    Tomer Michaeli

  3. Kyoto University, Kyoto, Japan

    Ko Nishino

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Peng, HW., Tseng, YC. (2023). Multi-scale Motion-Aware Module for Video Action Recognition. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13806. Springer, Cham. https://doi.org/10.1007/978-3-031-25075-0_40

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  • DOI: https://doi.org/10.1007/978-3-031-25075-0_40

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