Transfer Learning for Cross-dataset Isolated Sign Language Recognition in Under-Resourced Datasets

• URL: http://arxiv.org/abs/2403.14534v2
• Date: Mon, 15 Apr 2024 15:30:31 GMT
• Title: Transfer Learning for Cross-dataset Isolated Sign Language Recognition in Under-Resourced Datasets
• Authors: Ahmet Alp Kindiroglu, Ozgur Kara, Ogulcan Ozdemir, Lale Akarun,
• Abstract summary: We use a temporal graph convolution-based sign language recognition approach to evaluate five supervised transfer learning approaches. Experiments demonstrate that improvement over finetuning based transfer learning is possible with specialized supervised transfer learning methods.
• Score: 2.512406961007489
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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