SignRep: Enhancing Self-Supervised Sign Representations

• URL: http://arxiv.org/abs/2503.08529v1
• Date: Tue, 11 Mar 2025 15:20:01 GMT
• Title: SignRep: Enhancing Self-Supervised Sign Representations
• Authors: Ryan Wong, Necati Cihan Camgoz, Richard Bowden,
• Abstract summary: Sign language representation learning presents unique challenges due to the complex-temporal nature of signs and the scarcity of labeled datasets.<n>We introduce a scalable, self-supervised framework for sign representation learning.<n>Our model does not require skeletal keypoints during inference, avoiding the limitations of key-point-based models during downstream tasks.<n>It excels in sign dictionary retrieval and sign translation, surpassing standard MAE pre-training and skeletal-based representations in retrieval.
• Score: 30.008980708977095
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Sign language representation learning presents unique challenges due to the complex spatio-temporal nature of signs and the scarcity of labeled datasets. Existing methods often rely either on models pre-trained on general visual tasks, that lack sign-specific features, or use complex multimodal and multi-branch architectures. To bridge this gap, we introduce a scalable, self-supervised framework for sign representation learning. We leverage important inductive (sign) priors during the training of our RGB model. To do this, we leverage simple but important cues based on skeletons while pretraining a masked autoencoder. These sign specific priors alongside feature regularization and an adversarial style agnostic loss provide a powerful backbone. Notably, our model does not require skeletal keypoints during inference, avoiding the limitations of keypoint-based models during downstream tasks. When finetuned, we achieve state-of-the-art performance for sign recognition on the WLASL, ASL-Citizen and NMFs-CSL datasets, using a simpler architecture and with only a single-modality. Beyond recognition, our frozen model excels in sign dictionary retrieval and sign translation, surpassing standard MAE pretraining and skeletal-based representations in retrieval. It also reduces computational costs for training existing sign translation models while maintaining strong performance on Phoenix2014T, CSL-Daily and How2Sign.

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