CLIP-SLA: Parameter-Efficient CLIP Adaptation for Continuous Sign Language Recognition

Sarah Alyami; Hamzah Luqman

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arXiv · 2504.01666

CLIP-SLA: Parameter-Efficient CLIP Adaptation for Continuous Sign Language Recognition

Sarah Alyami·Hamzah Luqman

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Abstract

Continuous sign language recognition (CSLR) focuses on interpreting and transcribing sequences of sign language gestures in videos. In this work, we propose CLIP sign language adaptation (CLIP-SLA), a novel CSLR framework that leverages the powerful pre-trained visual encoder from the CLIP model to sign language tasks through parameter-efficient fine-tuning (PEFT). We introduce two variants, SLA-Adapter and SLA-LoRA, which integrate PEFT modules into the CLIP visual encoder, enabling fine-tuning with minimal trainable parameters. The effectiveness of the proposed frameworks is validated on four datasets: Phoenix2014, Phoenix2014-T, CSL-Daily, and Isharah-500, where both CLIP-SLA variants outperformed several SOTA models with fewer trainable parameters. Extensive ablation studies emphasize the effectiveness and flexibility of the proposed methods with different vision-language models for CSLR. These findings showcase the potential of adapting large-scale pre-trained models for scalable and efficient CSLR, which pave the way for future advancements in sign language understanding.

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BibTeX RIS

Sarah Alyami, Hamzah Luqman. 2025-04-02. CLIP-SLA: Parameter-Efficient CLIP Adaptation for Continuous Sign Language Recognition. https://arxiv.org/abs/2504.01666

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