Despite the recent success of deep learning in video-related tasks, deep models typically
focus on the most discriminative features, ignoring other potentially non-trivial and …

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 …

Despite the recent success of deep learning in continuous sign language recognition
(CSLR), deep models typically focus on the most discriminative features, ignoring other …

Word-level sign language recognition (SLR) is a significant task which transcribes a sign
language video into a word. Currently, deep-learning-based frameworks mostly combine …

Sign language recognition (SLR) plays a crucial role in bridging the communication gap
between the hearing and vocally impaired community and the rest of the society. Word-level …

Since the superiority of Transformer in learning long-term dependency, the sign language
Transformer model achieves remarkable progress in Sign Language Recognition (SLR) and …

Sign language recognition (SLR) is a weakly supervised task that annotates sign videos as
textual glosses. Recent studies show that insufficient training caused by the lack of large …

Vision-based sign language recognition aims at helping the hearing-impaired people to
communicate with others. However, most existing sign language datasets are limited to a …

Abstract Continuous Sign Language Recognition (CSLR) aims to transcribe the signs of an
untrimmed video into written words or glosses. The mainstream framework for CSLR …

Sign languages are visual languages which convey information by signers' handshape,
facial expression, body movement, and so forth. Due to the inherent restriction of …