VTaMo: Video-Text Alignment Model for Sign Language Translation
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Computer Science > Computer Vision and Pattern Recognition
Title:VTaMo: Video-Text Alignment Model for Sign Language Translation
Abstract:Sign language translation (SLT) converts continuous sign videos into spoken language text. Gloss-free approaches leverage pre-trained visual encoders and language models but rely on implicit cross-modal alignment from translation supervision alone. We present VTaMo, a framework that introduces explicit multi-granularity alignment at three levels: (1) local alignment via entropy-regularized optimal transport with a learnable null token for fine-grained frame-to-token correspondences; (2) global alignment via a learnable orthogonal transformation that calibrates embedding space geometry through Earth Mover's Distance; and (3) position-aligned contrastive learning for discriminative token-level representations. Experiments on Phoenix-2014T, CSL-Daily, How2Sign, and OpenASL demonstrate consistent state-of-the-art performance, with ablations confirming the complementary contributions of each component. Code is available at this https URL.
| Comments: | 18 pages, 5 figures, 8 tables. Accepted to ECCV 2026 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL) |
| Cite as: | arXiv:2607.09126 [cs.CV] |
| (or arXiv:2607.09126v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2607.09126
arXiv-issued DOI via DataCite (pending registration)
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