Learning Shape-Invariant Representation for Generalizable Semantic Segmentation-Reference-Cited by-同舟云学术

Learning Shape-Invariant Representation for Generalizable Semantic Segmentation

Published:2023 Issue: Volume:32 Page:5031-5045
ISSN:1057-7149
Container-title:IEEE Transactions on Image Processing
language:
Short-container-title:IEEE Trans. on Image Process.

Author:

Zhang Yuhang¹^ORCID,Tian Shishun¹^ORCID,Liao Muxin¹^ORCID,Hua Guoguang¹,Zou Wenbin²^ORCID,Xu Chen³^ORCID

Affiliation:

1. Guangdong Key Laboratory of Intelligent Information Processing, College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China

2. Guangdong Key Laboratory of Intelligent Information Processing and the Shenzhen Key Laboratory of Advanced Machine Learning and Applications, College of Electronics and Information Engineering, Institute of Artificial Intelligence and Advanced Communication, Shenzhen University, Shenzhen, China

3. College of Mathematics and Statistics, Shenzhen University, Shenzhen, China

Funder

National Natural Science Foundation of China

Key Project of Department of Education of Guangdong Province

Natural Science Foundation of Guangdong Province, China

Key Project of Shenzhen Science and Technology Plan

Interdisciplinary Innovation Team of Shenzhen University

Tencent “Rhinoceros Birds”–Scientific Research Foundation for Young Teachers of Shenzhen University, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Graphics and Computer-Aided Design,Software