Continuous Sign Language Recognition Based on Motor attention mechanism and frame-level Self-distillation

TL;DR

This paper introduces a novel holistic model for continuous sign language recognition that combines a motor attention mechanism to capture dynamic local motions with frame-level self-distillation to enhance feature extraction, achieving state-of-the-art results.

Contribution

It proposes a new model integrating motor attention and self-distillation for improved CSLR accuracy without extra computational cost.

Findings

Achieved state-of-the-art accuracy on three datasets.

Effectively captures dynamic motion changes in sign language.

Enhances feature representation through self-distillation.

Abstract

Changes in facial expression, head movement, body movement and gesture movement are remarkable cues in sign language recognition, and most of the current continuous sign language recognition(CSLR) research methods mainly focus on static images in video sequences at the frame-level feature extraction stage, while ignoring the dynamic changes in the images. In this paper, we propose a novel motor attention mechanism to capture the distorted changes in local motion regions during sign language expression, and obtain a dynamic representation of image changes. And for the first time, we apply the self-distillation method to frame-level feature extraction for continuous sign language, which improves the feature expression without increasing the computational resources by self-distilling the features of adjacent stages and using the higher-order features as teachers to guide the lower-order features. The combination of the two constitutes our proposed holistic model of CSLR Based on motor attention mechanism and frame-level Self-Distillation (MAM-FSD), which improves the inference ability and robustness of the model. We conduct experiments on three publicly available datasets, and the experimental results show that our proposed method can effectively extract the sign language motion information in videos, improve the accuracy of CSLR and reach the state-of-the-art level.