  Sign language serves as a non-vocal means of communication, transmitting
information and significance through gestures, facial expressions, and bodily
movements. The majority of current approaches for sign language recognition
(SLR) and translation rely on RGB video inputs, which are vulnerable to
fluctuations in the background. Employing a keypoint-based strategy not only
mitigates the effects of background alterations but also substantially
diminishes the computational demands of the model. Nevertheless, contemporary
keypoint-based methodologies fail to fully harness the implicit knowledge
embedded in keypoint sequences. To tackle this challenge, our inspiration is
derived from the human cognition mechanism, which discerns sign language by
analyzing the interplay between gesture configurations and supplementary
elements. We propose a multi-stream keypoint attention network to depict a
sequence of keypoints produced by a readily available keypoint estimator. In
order to facilitate interaction across multiple streams, we investigate diverse
methodologies such as keypoint fusion strategies, head fusion, and
self-distillation. The resulting framework is denoted as MSKA-SLR, which is
expanded into a sign language translation (SLT) model through the
straightforward addition of an extra translation network. We carry out
comprehensive experiments on well-known benchmarks like Phoenix-2014,
Phoenix-2014T, and CSL-Daily to showcase the efficacy of our methodology.
Notably, we have attained a novel state-of-the-art performance in the sign
language translation task of Phoenix-2014T. The code and models can be accessed
at: https://github.com/sutwangyan/MSKA.
