Transfer Learning for Cross-dataset Isolated Sign Language Recognition in Under-Resourced Datasets
<|reference_start|>Transfer Learning for Cross-dataset Isolated Sign Language Recognition in Under-Resourced Datasets: 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.<|reference_end|>
@article{kindiroglu2024transfer,
    title={Transfer Learning for Cross-dataset Isolated Sign Language Recognition
  in Under-Resourced Datasets},
    author={Ahmet Alp Kindiroglu, Ozgur Kara, Ogulcan Ozdemir, Lale Akarun},
    journal={arXiv preprint arXiv:2403.14534},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14534},
    primaryClass={cs.CV}
}