Dynamic Adaptive Representation Calibration for Keypoint-Based Sign Language Recognition
Individual differences in signers’ speed and amplitude cause variations in keypoint sequences, leading to redundant features that degrade the classification performance of sign language recognition models.
To reduce the impact of individual differences among signers, this paper proposes a Dynamic Adaptive Representation Calibration (DARC) module that helps the model focus on the most informative features.
The study utilizes component-based modeling of high-dimensional semantic features and introduces sample-adaptive learnable weights. The method was validated using large-scale sign language datasets, specifically WLASL2000 and MSASL1000.
The proposed DARC module dynamically adjusts the importance of different features for each input, allowing the model to focus more on informative patterns and reducing the influence of individual differences among signers.
The proposed method achieved a Top-1 accuracy of 56.86% on the WLASL2000 dataset (a 3.2% relative improvement over the state-of-the-art DSTA-SLR) and 65.88% on the MSASL1000 dataset. Visualizations confirm that the proposed module significantly improves feature discriminability and clustering clarity. Additionally, multi-stream training validations demonstrate consistent performance improvements across spatial and temporal dynamic streams, effectively raising the model’s overall representation ceiling.
Developers of practical sign language recognition systems should consider integrating dynamic adaptive calibration to better handle natural variations among different signers in real-world environments.
Researchers should further explore dynamic feature recalibration techniques to improve model robustness against individual variations in sequence-based recognition tasks.
By improving the accuracy of keypoint-based computer vision models, this research helps develop more accessible and scalable tools to bridge the communication gap between the deaf/hard-of-hearing community and hearing individuals.
Future studies should explore the application of the DARC module to other video-based action recognition tasks or investigate its performance in real-time continuous sign language translation.
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