  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.
