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

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.

A Comparative Study of Continuous Sign Language Recognition Techniques

Continuous Sign Language Recognition (CSLR) focuses on the interpretation of a sequence of sign language gestures performed continually without pauses. In this study, we conduct an empirical evaluation of recent deep learning CSLR techniques and assess their performance across various datasets and sign languages. The models selected for analysis implement a range of approaches for extracting meaningful features and employ distinct training strategies. To determine their efficacy in modeling different sign languages, these models were evaluated using multiple datasets, specifically RWTH-PHOENIX-Weather-2014, ArabSign, and GrSL, each representing a unique sign language. The performance of the models was further tested with unseen signers and sentences. The conducted experiments establish new benchmarks on the selected datasets and provide valuable insights into the robustness and generalization of the evaluated techniques under challenging scenarios.