Visual Alignment Constraint for Continuous Sign Language Recognition

Abstract

Vision-based Continuous Sign Language Recognition (CSLR) aims to recognize unsegmented gestures from image sequences. To better train CSLR models, the iterative training scheme is widely adopted to alleviate the overfitting of the alignment model. Although the iterative training scheme can improve performance, it will also increase the training time. In this work, we revisit the overfitting problem in recent CTC-based CSLR works and attribute it to the insufficient training of the feature extractor. To solve this problem, we propose a Visual Alignment Constraint (VAC) to enhance the feature extractor with more alignment supervision. Specifically, the proposed VAC is composed of two auxiliary losses: one makes predictions based on visual features only, and the other aligns short-term visual and longterm contextual features. Moreover, we further propose two metrics to evaluate the contributions of the feature extractor and the alignment model, which provide evidence for the overfitting problem. The proposed VAC achieves competitive performance on two challenging CSLR datasets and experimental results show its effectiveness.