Exploiting Ensemble Learning for Cross-View Isolated Sign Language Recognition
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913677919125504 |
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| author | Wang, Fei Li, Kun Nie, Yiqi Duan, Zhangling Zou, Peng Wu, Zhiliang Wang, Yuwei Wei, Yanyan |
| author_facet | Wang, Fei Li, Kun Nie, Yiqi Duan, Zhangling Zou, Peng Wu, Zhiliang Wang, Yuwei Wei, Yanyan |
| contents | In this paper, we present our solution to the Cross-View Isolated Sign Language Recognition (CV-ISLR) challenge held at WWW 2025. CV-ISLR addresses a critical issue in traditional Isolated Sign Language Recognition (ISLR), where existing datasets predominantly capture sign language videos from a frontal perspective, while real-world camera angles often vary. To accurately recognize sign language from different viewpoints, models must be capable of understanding gestures from multiple angles, making cross-view recognition challenging. To address this, we explore the advantages of ensemble learning, which enhances model robustness and generalization across diverse views. Our approach, built on a multi-dimensional Video Swin Transformer model, leverages this ensemble strategy to achieve competitive performance. Finally, our solution ranked 3rd in both the RGB-based ISLR and RGB-D-based ISLR tracks, demonstrating the effectiveness in handling the challenges of cross-view recognition. The code is available at: https://github.com/Jiafei127/CV_ISLR_WWW2025. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_02196 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploiting Ensemble Learning for Cross-View Isolated Sign Language Recognition Wang, Fei Li, Kun Nie, Yiqi Duan, Zhangling Zou, Peng Wu, Zhiliang Wang, Yuwei Wei, Yanyan Computer Vision and Pattern Recognition Artificial Intelligence In this paper, we present our solution to the Cross-View Isolated Sign Language Recognition (CV-ISLR) challenge held at WWW 2025. CV-ISLR addresses a critical issue in traditional Isolated Sign Language Recognition (ISLR), where existing datasets predominantly capture sign language videos from a frontal perspective, while real-world camera angles often vary. To accurately recognize sign language from different viewpoints, models must be capable of understanding gestures from multiple angles, making cross-view recognition challenging. To address this, we explore the advantages of ensemble learning, which enhances model robustness and generalization across diverse views. Our approach, built on a multi-dimensional Video Swin Transformer model, leverages this ensemble strategy to achieve competitive performance. Finally, our solution ranked 3rd in both the RGB-based ISLR and RGB-D-based ISLR tracks, demonstrating the effectiveness in handling the challenges of cross-view recognition. The code is available at: https://github.com/Jiafei127/CV_ISLR_WWW2025. |
| title | Exploiting Ensemble Learning for Cross-View Isolated Sign Language Recognition |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2502.02196 |