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VTaMo introduces explicit multi-granularity video-text alignment for sign language translation using optimal transport and contrastive learning, achieving state-of-the-art performance on four benchmarks.
This paper presents a sentence-level sign language translation system fine-tuned with QLoRA on a subset of How2Sign, achieving BLEU 15.9. Its main contribution is a hardware-aware streaming pipeline using a Raspberry Pi 4B client and a CPU/GPU backend, reducing mean latency by 27.71%.