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About this paper
Hezhen Hu, Weichao Zhao, Wengang Zhou, Houqiang Li
SignBERT+ presents a self-supervised pre-training framework for Sign Language Understanding, integrating hand-model-aware prior knowledge to learn robust representations from unlabeled sign pose data. It achieves new state-of-the-art performance across isolated sign language recognition (e.g., 73.71% Top-1 accuracy on MSASL with RGB fusion), continuous sign language recognition, and sign language translation tasks.
Key insights
- Masked modeling, when adapted to continuous visual sign language data with multi-level masking strategies, effectively learns robust, context-aware hand representations from unlabeled videos.
- Integrating explicit hand prior knowledge through a model-aware decoder (MANO) guides the framework to reconstruct geometrically plausible and semantically meaningful hand configurations, even from severely corrupted inputs.
- Combining spatial and temporal position encodings yields substantial performance gains, highlighting their importance in enabling the Transformer encoder to capture fine-grained gesture state and spatio-temporal dynamics.
Summary by alphaXiv.
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