A corpus augmentation pipeline using CTC clip extraction, LLM-generated gloss sentences, and random video stitching improves BLEU-4 by 2.92 on the GFSLT-VLP baseline for sign language translation.
Sign2gpt: Leveraging large language models for gloss-free sign language translation
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SLU-2K is a new closed-ended QA benchmark that measures semantic understanding in sign language video, showing MLLMs near random and fine-tuned SOTA systems at 56.7-75.2% accuracy.
A pair selection strategy based on negative similarity dynamics strengthens contrastive supervision in gloss-free sign language translation by reducing noisy negatives.
Constructs continuous sign conversation data from isolated signs using retrieval and diffusion models to train a direct sign-to-sign conversational AI.
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Corpus Augmentation for Sign Language Translation via LLM-Guided Video Stitching
A corpus augmentation pipeline using CTC clip extraction, LLM-generated gloss sentences, and random video stitching improves BLEU-4 by 2.92 on the GFSLT-VLP baseline for sign language translation.