Continuous Sign Language Recognition Based on Motor attention mechanism
  and frame-level Self-distillation
Qidan Zhu Jing Li Fei Yuan Quan Gan
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.