Transfer Learning for Cross-dataset Isolated Sign Language Recognition in Under-Resourced Datasets
Abstract
Sign language recognition (SLR) has recently achieved a breakthrough in performance thanks to deep neural networks trained on large annotated sign datasets. Of the many different sign languages, these annotated datasets are only available for a select few. Since acquiring gloss-level labels on sign language videos is difficult, learning by transferring knowledge from existing annotated sources is useful for recognition in under-resourced sign languages. This study provides a publicly available cross-dataset transfer learning benchmark from two existing public Turkish SLR datasets. We use a temporal graph convolution-based sign language recognition approach to evaluate five supervised transfer learning approaches and experiment with closed-set and partial-set cross-dataset transfer learning. Experiments demonstrate that improvement over finetuning based transfer learning is possible with specialized supervised transfer learning methods.
Cite
@article{arxiv.2403.14534,
title = {Transfer Learning for Cross-dataset Isolated Sign Language Recognition in Under-Resourced Datasets},
author = {Ahmet Alp Kindiroglu and Ozgur Kara and Ogulcan Ozdemir and Lale Akarun},
journal= {arXiv preprint arXiv:2403.14534},
year = {2024}
}
Comments
Accepted to The 18th IEEE International Conference on Automatic Face and Gesture Recognition 2024, Code available in https://github.com/alpk/tid-supervised-transfer-learning-dataset