SignBERT: Pre-Training of Hand-Model-Aware Representation for Sign Language Recognition

• URL: http://arxiv.org/abs/2110.05382v1
• Date: Mon, 11 Oct 2021 16:18:09 GMT
• Title: SignBERT: Pre-Training of Hand-Model-Aware Representation for Sign Language Recognition
• Authors: Hezhen Hu, Weichao Zhao, Wengang Zhou, Yuechen Wang, Houqiang Li
• Abstract summary: Hand gesture serves as a critical role in sign language. Current deep-learning-based sign language recognition methods may suffer insufficient interpretability. We introduce the first self-supervised pre-trainable SignBERT with incorporated hand prior for SLR.
• Score: 94.30084702921529
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hand gesture serves as a critical role in sign language. Current deep-learning-based sign language recognition (SLR) methods may suffer insufficient interpretability and overfitting due to limited sign data sources. In this paper, we introduce the first self-supervised pre-trainable SignBERT with incorporated hand prior for SLR. SignBERT views the hand pose as a visual token, which is derived from an off-the-shelf pose extractor. The visual tokens are then embedded with gesture state, temporal and hand chirality information. To take full advantage of available sign data sources, SignBERT first performs self-supervised pre-training by masking and reconstructing visual tokens. Jointly with several mask modeling strategies, we attempt to incorporate hand prior in a model-aware method to better model hierarchical context over the hand sequence. Then with the prediction head added, SignBERT is fine-tuned to perform the downstream SLR task. To validate the effectiveness of our method on SLR, we perform extensive experiments on four public benchmark datasets, i.e., NMFs-CSL, SLR500, MSASL and WLASL. Experiment results demonstrate the effectiveness of both self-supervised learning and imported hand prior. Furthermore, we achieve state-of-the-art performance on all benchmarks with a notable gain.

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