Hands-On: Segmenting Individual Signs from Continuous Sequences
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866913161951576064 |
|---|---|
| author | Low, JianHe Walsh, Harry Sincan, Ozge Mercanoglu Bowden, Richard |
| author_facet | Low, JianHe Walsh, Harry Sincan, Ozge Mercanoglu Bowden, Richard |
| contents | This work tackles the challenge of continuous sign language segmentation, a key task with huge implications for sign language translation and data annotation. We propose a transformer-based architecture that models the temporal dynamics of signing and frames segmentation as a sequence labeling problem using the Begin-In-Out (BIO) tagging scheme. Our method leverages the HaMeR hand features, and is complemented with 3D Angles. Extensive experiments show that our model achieves state-of-the-art results on the DGS Corpus, while our features surpass prior benchmarks on BSLCorpus. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08593 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hands-On: Segmenting Individual Signs from Continuous Sequences Low, JianHe Walsh, Harry Sincan, Ozge Mercanoglu Bowden, Richard Computer Vision and Pattern Recognition Artificial Intelligence This work tackles the challenge of continuous sign language segmentation, a key task with huge implications for sign language translation and data annotation. We propose a transformer-based architecture that models the temporal dynamics of signing and frames segmentation as a sequence labeling problem using the Begin-In-Out (BIO) tagging scheme. Our method leverages the HaMeR hand features, and is complemented with 3D Angles. Extensive experiments show that our model achieves state-of-the-art results on the DGS Corpus, while our features surpass prior benchmarks on BSLCorpus. |
| title | Hands-On: Segmenting Individual Signs from Continuous Sequences |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2504.08593 |