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2020 | OriginalPaper | Chapter

Fully Convolutional Networks for Continuous Sign Language Recognition

Authors : Ka Leong Cheng, Zhaoyang Yang, Qifeng Chen, Yu-Wing Tai

Published in: Computer Vision – ECCV 2020

Publisher: Springer International Publishing

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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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Title: Fully Convolutional Networks for Continuous Sign Language Recognition
Authors: Ka Leong Cheng
Zhaoyang Yang
Qifeng Chen
Yu-Wing Tai
Publisher: Springer International Publishing
Book: Computer Vision – ECCV 2020
Print ISBN: 978-3-030-58585-3

Electronic ISBN: 978-3-030-58586-0

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-58586-0_41

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