Self-Supervised Representation Learning With Spatial-Temporal Consistency for Sign Language Recognition-Reference-Cited by-同舟云学术

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Self-Supervised Representation Learning With Spatial-Temporal Consistency for Sign Language Recognition

Published:2024 Issue: Volume:33 Page:4188-4201
ISSN:1057-7149
Container-title:IEEE Transactions on Image Processing
language:
Short-container-title:IEEE Trans. on Image Process.

Author:

Zhao Weichao¹^ORCID,Zhou Wengang¹^ORCID,Hu Hezhen²^ORCID,Wang Min³^ORCID,Li Houqiang¹^ORCID

Affiliation:

1. MoE Key Laboratory of Brain-Inspired Intelligent Perception and Cognition, University of Science and Technology of China, Hefei, China

2. Visual Informatics Group, University of Texas at Austin, Austin, TX, USA

3. Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China

Funder

National Natural Science Foundation of China

Youth Innovation Promotion Association Chinese Academy of Sciences

Graphics Processing Unit (GPU) cluster built by the Multimedia Computing and Communication (MCC) Laboratory of Information Science and Technology Institution, USTC

Supercomputing Center of the USTC

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/83/10346232/10571834.pdf?arnumber=10571834

Reference76 articles.

1. Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison

2. Transferring Cross-Domain Knowledge for Video Sign Language Recognition

3. Deep Sign: Enabling Robust Statistical Continuous Sign Language Recognition via Hybrid CNN-HMMs

4. MS-ASL: A large-scale data set and benchmark for understanding American sign language;Joze

5. Pose-based Sign Language Recognition using GCN and BERT

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