mlsanityData to 14 Oct 2026

Geo-Sign: Hyperbolic Contrastive Regularisation for Geometrically Aware Sign Language Translation

arXiv 2506.00129 ↗First posted 30 May 2025cs.CV cs.LGcited by 12

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Abstract (arXiv)

Recent progress in Sign Language Translation (SLT) has focussed primarily on improving the representational capacity of large language models to incorporate Sign Language features. This work explores an alternative direction: enhancing the geometric properties of skeletal representations themselves. We propose Geo-Sign, a method that leverages the properties of hyperbolic geometry to model the hierarchical structure inherent in sign language kinematics. By projecting skeletal features derived from Spatio-Temporal Graph Convolutional Networks (ST-GCNs) into the Poincar'e ball model, we aim to create more discriminative embeddings, particularly for fine-grained motions like finger articulations. We introduce a hyperbolic projection layer, a weighted Fr'echet mean aggregation scheme, and a geometric contrastive loss operating directly in hyperbolic space. These components are integrated into an end-to-end translation framework as a regularisation function, to enhance the representations within the language model. This work demonstrates the potential of hyperbolic geometry to improve skeletal representations for Sign Language Translation, improving on SOTA RGB methods while preserving privacy and improving computational efficiency. Code available here: https://github.com/ed-fish/geo-sign.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 66 of its 88 references that are in the map. First posted on arXiv 30 May 2025.

Builds on

66 of 88 references.

References · 66

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38 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 8.

12 papers.