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arXiv · 2309.02450

Self-Supervised Video Transformers for Isolated Sign Language Recognition

Marcelo Sandoval-Castaneda·Yanhong Li·Diane Brentari·Karen Livescu·Gregory Shakhnarovich
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Abstract

This paper presents an in-depth analysis of various self-supervision methods for isolated sign language recognition (ISLR). We consider four recently introduced transformer-based approaches to self-supervised learning from videos, and four pre-training data regimes, and study all the combinations on the WLASL2000 dataset. Our findings reveal that MaskFeat achieves performance superior to pose-based and supervised video models, with a top-1 accuracy of 79.02% on gloss-based WLASL2000. Furthermore, we analyze these models' ability to produce representations of ASL signs using linear probing on diverse phonological features. This study underscores the value of architecture and pre-training task choices in ISLR. Specifically, our results on WLASL2000 highlight the power of masked reconstruction pre-training, and our linear probing results demonstrate the importance of hierarchical vision transformers for sign language representation.

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Marcelo Sandoval-Castaneda, Yanhong Li, Diane Brentari, Karen Livescu, Gregory Shakhnarovich. 2023-09-02. Self-Supervised Video Transformers for Isolated Sign Language Recognition. https://arxiv.org/abs/2309.02450

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