  Sign languages are visual languages using manual articulations and non-manual
elements to convey information. For sign language recognition and translation,
the majority of existing approaches directly encode RGB videos into hidden
representations. RGB videos, however, are raw signals with substantial visual
redundancy, leading the encoder to overlook the key information for sign
language understanding. To mitigate this problem and better incorporate domain
knowledge, such as handshape and body movement, we introduce a dual visual
encoder containing two separate streams to model both the raw videos and the
keypoint sequences generated by an off-the-shelf keypoint estimator. To make
the two streams interact with each other, we explore a variety of techniques,
including bidirectional lateral connection, sign pyramid network with auxiliary
supervision, and frame-level self-distillation. The resulting model is called
TwoStream-SLR, which is competent for sign language recognition (SLR).
TwoStream-SLR is extended to a sign language translation (SLT) model,
TwoStream-SLT, by simply attaching an extra translation network.
Experimentally, our TwoStream-SLR and TwoStream-SLT achieve state-of-the-art
performance on SLR and SLT tasks across a series of datasets including
Phoenix-2014, Phoenix-2014T, and CSL-Daily. Code and models are available at:
https://github.com/FangyunWei/SLRT.
