Sign Language Translation with Hierarchical Spatio-TemporalGraph Neural Network

• URL: http://arxiv.org/abs/2111.07258v1
• Date: Sun, 14 Nov 2021 07:02:28 GMT
• Title: Sign Language Translation with Hierarchical Spatio-TemporalGraph Neural Network
• Authors: Jichao Kan, Kun Hu, Markus Hagenbuchner, Ah Chung Tsoi, Mohammed Bennamounm, Zhiyong Wang
• Abstract summary: Sign language translation (SLT) generates text in a spoken language from visual content in a sign language. In this paper, these unique characteristics of sign languages are formulated as hierarchical-temporal graph representations. A novel deep learning architecture, namely hierarchical hierarchical-temporal graph neural network (HSTG-NN), is proposed.
• Score: 6.623802929157273
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sign language translation (SLT), which generates text in a spoken language from visual content in a sign language, is important to assist the hard-of-hearing community for their communications. Inspired by neural machine translation (NMT), most existing SLT studies adopted a general sequence to sequence learning strategy. However, SLT is significantly different from general NMT tasks since sign languages convey messages through multiple visual-manual aspects. Therefore, in this paper, these unique characteristics of sign languages are formulated as hierarchical spatio-temporal graph representations, including high-level and fine-level graphs of which a vertex characterizes a specified body part and an edge represents their interactions. Particularly, high-level graphs represent the patterns in the regions such as hands and face, and fine-level graphs consider the joints of hands and landmarks of facial regions. To learn these graph patterns, a novel deep learning architecture, namely hierarchical spatio-temporal graph neural network (HST-GNN), is proposed. Graph convolutions and graph self-attentions with neighborhood context are proposed to characterize both the local and the global graph properties. Experimental results on benchmark datasets demonstrated the effectiveness of the proposed method.

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