Training Strategies for Isolated Sign Language Recognition
This work addresses communication accessibility for deaf and hard-of-hearing individuals by improving sign language recognition, though it is incremental as it builds on existing methods with specific enhancements.
The paper tackles challenges in Isolated Sign Language Recognition (ISLR) such as low data quality and variable gesturing speeds by introducing a training pipeline with image/video augmentations and a regression head with IoU-balanced loss, achieving state-of-the-art results on WLASL and Slovo datasets.
Accurate recognition and interpretation of sign language are crucial for enhancing communication accessibility for deaf and hard of hearing individuals. However, current approaches of Isolated Sign Language Recognition (ISLR) often face challenges such as low data quality and variability in gesturing speed. This paper introduces a comprehensive model training pipeline for ISLR designed to accommodate the distinctive characteristics and constraints of the Sign Language (SL) domain. The constructed pipeline incorporates carefully selected image and video augmentations to tackle the challenges of low data quality and varying sign speeds. Including an additional regression head combined with IoU-balanced classification loss enhances the model's awareness of the gesture and simplifies capturing temporal information. Extensive experiments demonstrate that the developed training pipeline easily adapts to different datasets and architectures. Additionally, the ablation study shows that each proposed component expands the potential to consider ISLR task specifics. The presented strategies enhance recognition performance across various ISLR benchmarks and achieve state-of-the-art results on the WLASL and Slovo datasets.