STARK: Spatio-Temporal Attention for Representation of Keypoints for Continuous Sign Language Recognition
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| Format: | Preprint |
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2026
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| _version_ | 1866912970746888192 |
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| author | Patra, Suvajit Samanta, Soumitra |
| author_facet | Patra, Suvajit Samanta, Soumitra |
| contents | Continuous Sign Language Recognition (CSLR) is a crucial task for understanding the languages of deaf communities. Contemporary keypoint-based approaches typically rely on spatio-temporal encoding, where spatial interactions among keypoints are modeled using Graph Convolutional Networks or attention mechanisms, while temporal dynamics are captured using 1D convolutional networks. However, such designs often introduce a large number of parameters in both the encoder and the decoder. This paper introduces a unified spatio-temporal attention network that computes attention scores both spatially (across keypoints) and temporally (within local windows), and aggregates features to produce a local context-aware spatio-temporal representation. The proposed encoder contains approximately $70-80\%$ fewer parameters than existing state-of-the-art models while achieving comparable performance to keypoint-based methods on the Phoenix-14T dataset. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_16163 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | STARK: Spatio-Temporal Attention for Representation of Keypoints for Continuous Sign Language Recognition Patra, Suvajit Samanta, Soumitra Computer Vision and Pattern Recognition Computation and Language Continuous Sign Language Recognition (CSLR) is a crucial task for understanding the languages of deaf communities. Contemporary keypoint-based approaches typically rely on spatio-temporal encoding, where spatial interactions among keypoints are modeled using Graph Convolutional Networks or attention mechanisms, while temporal dynamics are captured using 1D convolutional networks. However, such designs often introduce a large number of parameters in both the encoder and the decoder. This paper introduces a unified spatio-temporal attention network that computes attention scores both spatially (across keypoints) and temporally (within local windows), and aggregates features to produce a local context-aware spatio-temporal representation. The proposed encoder contains approximately $70-80\%$ fewer parameters than existing state-of-the-art models while achieving comparable performance to keypoint-based methods on the Phoenix-14T dataset. |
| title | STARK: Spatio-Temporal Attention for Representation of Keypoints for Continuous Sign Language Recognition |
| topic | Computer Vision and Pattern Recognition Computation and Language |
| url | https://arxiv.org/abs/2603.16163 |