Logos as a Well-Tempered Pre-train for Sign Language Recognition
Ilya Ovodov, Petr Surovtsev, Karina Kvanchiani, Alexander Kapitanov, Alexander Nagaev

TL;DR
This paper introduces Logos, a large and well-annotated Russian Sign Language dataset, demonstrating that pre-training on it enhances sign language recognition across languages and improves accuracy, especially for low-resource datasets.
Contribution
It presents the Logos dataset as the largest RSL dataset, and shows that pre-training on it serves as a universal encoder for various sign language recognition tasks, including cross-language transfer learning.
Findings
Pre-trained models on Logos outperform state-of-the-art on WLASL.
Joint training with multiple classification heads improves low-resource dataset accuracy.
Explicitly labeling visually similar signs enhances model quality.
Abstract
This paper examines two aspects of the isolated sign language recognition (ISLR) task. First, although a certain number of datasets is available, the data for individual sign languages is limited. It poses the challenge of cross-language ISLR model training, including transfer learning. Second, similar signs can have different semantic meanings. It leads to ambiguity in dataset labeling and raises the question of the best policy for annotating such signs. To address these issues, this study presents Logos, a novel Russian Sign Language (RSL) dataset, the most extensive available ISLR dataset by the number of signers, one of the most extensive datasets in size and vocabulary, and the largest RSL dataset. It is shown that a model, pre-trained on the Logos dataset can be used as a universal encoder for other language SLR tasks, including few-shot learning. We explore cross-language…
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Taxonomy
TopicsHand Gesture Recognition Systems · Hearing Impairment and Communication · Speech and dialogue systems
MethodsSurrogate Lagrangian Relaxation
