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:this https URL.
View on arXiv@article{wang2025_2502.02196,
title={ Exploiting Ensemble Learning for Cross-View Isolated Sign Language Recognition },
author={ Fei Wang and Kun Li and Yiqi Nie and Zhangling Duan and Peng Zou and Zhiliang Wu and Yuwei Wang and Yanyan Wei },
journal={arXiv preprint arXiv:2502.02196},
year={ 2025 }
}