Self-Sufficient Framework for Continuous Sign Language Recognition
This work improves sign language recognition for accessibility applications, but it is incremental as it builds on existing methods with novel components.
The paper tackles the problem of Continuous Sign Language Recognition by addressing the need for multi-scale features and lack of frame-level annotations, achieving state-of-the-art performance on PHOENIX-2014 benchmarks among RGB-based methods.
The goal of this work is to develop self-sufficient framework for Continuous Sign Language Recognition (CSLR) that addresses key issues of sign language recognition. These include the need for complex multi-scale features such as hands, face, and mouth for understanding, and absence of frame-level annotations. To this end, we propose (1) Divide and Focus Convolution (DFConv) which extracts both manual and non-manual features without the need for additional networks or annotations, and (2) Dense Pseudo-Label Refinement (DPLR) which propagates non-spiky frame-level pseudo-labels by combining the ground truth gloss sequence labels with the predicted sequence. We demonstrate that our model achieves state-of-the-art performance among RGB-based methods on large-scale CSLR benchmarks, PHOENIX-2014 and PHOENIX-2014-T, while showing comparable results with better efficiency when compared to other approaches that use multi-modality or extra annotations.