Attention-Steered Vision-Language Models for Sign Language Translation

Meibo Hu; Guohao Sun; Annemarie D. Ross; Sheng Li; Zhiqiang Tao

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arXiv · 2608.00235

Attention-Steered Vision-Language Models for Sign Language Translation

Meibo Hu·Guohao Sun·Annemarie D. Ross·Sheng Li·Zhiqiang Tao

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Abstract

Vision-language models (VLMs) have emerged as a powerful framework for multimodal video understanding. However, they remain limited in the sign language translation task, where we identify a key failure mode of existing VLMbased translators: poor spatial-temporal visual grounding. In particular, we find that standard next-token cross-entropy does not directly provide signal for where and when the model should attend, causing models to overlook sign-relevant regions and frames. To address this challenge, we propose AttnSign, a VLM-based spatial-temporal attention steering framework for sign language translation. AttnSign first introduces spatial attention supervision for sign-relevant regions, such as face and hands, in each frame; then develops an RL-based motion-cadence steering method that encourages the model to explore and focus on sign-level keyframes. Experimental results on How2Sign and OpenASL benchmarks show that our proposed AttnSign consistently outperforms existing methods.

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BibTeX RIS

Meibo Hu, Guohao Sun, Annemarie D. Ross, Sheng Li, Zhiqiang Tao. 2026-09-16. Attention-Steered Vision-Language Models for Sign Language Translation. https://arxiv.org/abs/2608.00235

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