SignLlama enhances sign language translation by prioritizing visual features for LLMs

Researchers have developed SignLlama, a novel approach to enhance gloss-free sign language translation (GFSLT) by prioritizing visual features for large language models (LLMs). The method addresses two key challenges: bridging the gap between visual and textual inputs for LLMs and preventing models from overemphasizing text over visual cues. SignLlama introduces Filtered Pseudo-Gloss CTC Pretraining to supervise the visual backbone and a Visual-Prioritized Distillation strategy that forces the model to rely solely on visual inputs for generating target sequences. Experiments show SignLlama achieves competitive performance on GFSLT tasks without requiring additional modalities or pretraining datasets. AI

IMPACT This research could improve the accuracy and accessibility of sign language translation systems by better integrating visual data with LLMs.

RANK_REASON The cluster contains a research paper detailing a new method for sign language translation using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Filtered Pseudo-Gloss CTC Pretraining
• Gloss-Free Sign Language Translation
• large-language models
• SignLlama
• Visual-Prioritized Distillation

• paper
• model release

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