HAFormer: Unleashing the Power of Hierarchy-Aware Features for Lightweight Semantic Segmentation-Reference-Cited by-同舟云学术

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HAFormer: Unleashing the Power of Hierarchy-Aware Features for Lightweight Semantic Segmentation

Published:2024 Issue: Volume:33 Page:4202-4214
ISSN:1057-7149
Container-title:IEEE Transactions on Image Processing
language:
Short-container-title:IEEE Trans. on Image Process.

Author:

Xu Guoan¹^ORCID,Jia Wenjing¹^ORCID,Wu Tao²,Chen Ligeng²,Gao Guangwei³^ORCID

Affiliation:

1. Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia

2. State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing, China

3. Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing, China

Funder

Foundation of Key Laboratory of Artificial Intelligence of the Ministry of Education

Open Fund Project of the Provincial Key Laboratory for Computer Information Processing Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/83/10346232/10599205.pdf?arnumber=10599205

Reference68 articles.

1. Planning-oriented Autonomous Driving

2. An effective CNN and Transformer complementary network for medical image segmentation

3. Geometric Boundary Guided Feature Fusion and Spatial-Semantic Context Aggregation for Semantic Segmentation of Remote Sensing Images

4. Dual Attention Network for Scene Segmentation

5. Context and Spatial Feature Calibration for Real-Time Semantic Segmentation

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