Pose estimation keypoints are widely used in sign language recognition (SLR) as a means of generalising to unseen signers. Despite the advantages of keypoints, SLR models struggle to achieve high recognition accuracy for many signed languages due to the large degree of variability between occurrences of the same signs, the lack of large datasets and the imbalanced nature of the data therein. In this paper we seek to provide a deeper analysis into the ways that these keypoints are used by models in order to determine which are most informative to SLR, identify potentially redundant ones and investigate whether keypoints that are central to differentiating signs in practice are being effectively used as expected by models.
@inproceedings{holmes-etal-2024-keypoints:lrec,
author = {Holmes, Ruth M. and Rushe, Ellen and Ventresque, Anthony},
title = {The Key Points: Using Feature Importance to Identify Shortcomings in Sign Language Recognition Models},
pages = {15970--15975},
editor = {Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen},
booktitle = {2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation ({LREC-COLING} 2024)},
publisher = {{ELRA Language Resources Association (ELRA) and the International Committee on Computational Linguistics (ICCL)}},
address = {Torino, Italy},
day = {20--25},
month = may,
year = {2024},
isbn = {978-2-493814-10-4},
language = {english},
url = {https://aclanthology.org/2024.lrec-main.1387}
}