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Fully Convolutional Networks for Continuous Sign Language Recognition

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12369))

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Abstract

Continuous sign language recognition (SLR) is a challenging task that requires learning on both spatial and temporal dimensions of signing frame sequences. Most recent work accomplishes this by using CNN and RNN hybrid networks. However, training these networks is generally non-trivial, and most of them fail in learning unseen sequence patterns, causing an unsatisfactory performance for online recognition. In this paper, we propose a fully convolutional network (FCN) for online SLR to concurrently learn spatial and temporal features from weakly annotated video sequences with only sentence-level annotations given. A gloss feature enhancement (GFE) module is introduced in the proposed network to enforce better sequence alignment learning. The proposed network is end-to-end trainable without any pre-training. We conduct experiments on two large scale SLR datasets. Experiments show that our method for continuous SLR is effective and performs well in online recognition.

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Author information

Authors and Affiliations

  1. The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong

    Ka Leong Cheng, Qifeng Chen & Yu-Wing Tai

  2. Tencent, Shenzhen, China

    Zhaoyang Yang

  3. Kwai Inc., Beijing, China

    Yu-Wing Tai

Authors
  1. Ka Leong Cheng
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  2. Zhaoyang Yang
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  3. Qifeng Chen
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  4. Yu-Wing Tai
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Corresponding author

Correspondence to Yu-Wing Tai .

Editor information

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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Cheng, K.L., Yang, Z., Chen, Q., Tai, YW. (2020). Fully Convolutional Networks for Continuous Sign Language Recognition. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12369. Springer, Cham. https://doi.org/10.1007/978-3-030-58586-0_41

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  • DOI: https://doi.org/10.1007/978-3-030-58586-0_41

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