The NGT200 Dataset: Geometric Multi-View Isolated Sign Recognition
<|reference_start|>The NGT200 Dataset: Geometric Multi-View Isolated Sign Recognition: Sign Language Processing (SLP) provides a foundation for a more inclusive future in language technology; however, the field faces several significant challenges that must be addressed to achieve practical, real-world applications. This work addresses multi-view isolated sign recognition (MV-ISR), and highlights the essential role of 3D awareness and geometry in SLP systems. We introduce the NGT200 dataset, a novel spatio-temporal multi-view benchmark, establishing MV-ISR as distinct from single-view ISR (SV-ISR). We demonstrate the benefits of synthetic data and propose conditioning sign representations on spatial symmetries inherent in sign language. Leveraging an SE(2) equivariant model improves MV-ISR performance by 8%-22% over the baseline.<|reference_end|>
@article{ranum2024the,
    title={The NGT200 Dataset: Geometric Multi-View Isolated Sign Recognition},
    author={Oline Ranum and David R. Wessels and Gomer Otterspeer and Erik J.
  Bekkers and Floris Roelofsen and Jari I. Andersen},
    journal={arXiv preprint arXiv:2409.15284},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15284},
    primaryClass={cs.CV cs.CL}
}