@inproceedings{xu-etal-2024-ww-csl,
title = "{WW}-{CSL}: A New Dataset for Word-Based Wearable {C}hinese {S}ign {L}anguage Detection",
author = "Xu, Fan and
Liu, Kai and
Yang, Yifeng and
Yan, Keyu",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1541",
pages = "17718--17724",
abstract = "Sign language is an effective non-verbal communication mode for the hearing-impaired people. Since the video-based sign language detection models have high requirements for enough lighting and clear background, current wearing glove-based sign language models are robust for poor light and occlusion situations. In this paper, we annotate a new dataset of Word-based Wearable Chinese Sign Languag (WW-CSL) gestures. Specifically, we propose a three-form (e.g., sequential sensor data, gesture video, and gesture text) scheme to represent dynamic CSL gestures. Guided by the scheme, a total of 3,000 samples were collected, corresponding to 100 word-based CSL gestures. Furthermore, we present a transformer-based baseline model to fuse 2 inertial measurement unites (IMUs) and 10 flex sensors for the wearable CSL detection. In order to integrate the advantage of video-based and wearable glove-based CSL gestures, we also propose a transformer-based Multi-Modal CSL Detection (MM-CSLD) framework which adeptly integrates the local sequential sensor data derived from wearable-based CSL gestures with the global, fine-grained skeleton representations captured from video-based CSL gestures simultaneously.",
}
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<abstract>Sign language is an effective non-verbal communication mode for the hearing-impaired people. Since the video-based sign language detection models have high requirements for enough lighting and clear background, current wearing glove-based sign language models are robust for poor light and occlusion situations. In this paper, we annotate a new dataset of Word-based Wearable Chinese Sign Languag (WW-CSL) gestures. Specifically, we propose a three-form (e.g., sequential sensor data, gesture video, and gesture text) scheme to represent dynamic CSL gestures. Guided by the scheme, a total of 3,000 samples were collected, corresponding to 100 word-based CSL gestures. Furthermore, we present a transformer-based baseline model to fuse 2 inertial measurement unites (IMUs) and 10 flex sensors for the wearable CSL detection. In order to integrate the advantage of video-based and wearable glove-based CSL gestures, we also propose a transformer-based Multi-Modal CSL Detection (MM-CSLD) framework which adeptly integrates the local sequential sensor data derived from wearable-based CSL gestures with the global, fine-grained skeleton representations captured from video-based CSL gestures simultaneously.</abstract>
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%0 Conference Proceedings
%T WW-CSL: A New Dataset for Word-Based Wearable Chinese Sign Language Detection
%A Xu, Fan
%A Liu, Kai
%A Yang, Yifeng
%A Yan, Keyu
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F xu-etal-2024-ww-csl
%X Sign language is an effective non-verbal communication mode for the hearing-impaired people. Since the video-based sign language detection models have high requirements for enough lighting and clear background, current wearing glove-based sign language models are robust for poor light and occlusion situations. In this paper, we annotate a new dataset of Word-based Wearable Chinese Sign Languag (WW-CSL) gestures. Specifically, we propose a three-form (e.g., sequential sensor data, gesture video, and gesture text) scheme to represent dynamic CSL gestures. Guided by the scheme, a total of 3,000 samples were collected, corresponding to 100 word-based CSL gestures. Furthermore, we present a transformer-based baseline model to fuse 2 inertial measurement unites (IMUs) and 10 flex sensors for the wearable CSL detection. In order to integrate the advantage of video-based and wearable glove-based CSL gestures, we also propose a transformer-based Multi-Modal CSL Detection (MM-CSLD) framework which adeptly integrates the local sequential sensor data derived from wearable-based CSL gestures with the global, fine-grained skeleton representations captured from video-based CSL gestures simultaneously.
%U https://aclanthology.org/2024.lrec-main.1541
%P 17718-17724
Markdown (Informal)
[WW-CSL: A New Dataset for Word-Based Wearable Chinese Sign Language Detection](https://aclanthology.org/2024.lrec-main.1541) (Xu et al., LREC-COLING 2024)
ACL