Joint Graph Attention and Asymmetric Convolutional Neural Network for Deep Image Compression-Reference-Cited by-同舟云学术

Joint Graph Attention and Asymmetric Convolutional Neural Network for Deep Image Compression

Author:

Tang Zhisen¹,Wang Hanli¹^ORCID,Yi Xiaokai¹,Zhang Yun²^ORCID,Kwong Sam³^ORCID,Kuo C.-C. Jay⁴^ORCID

Affiliation:

1. Department of Computer Science and Technology, Tongji University, Shanghai, China

2. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

3. Department of Computer Science, City University of Hong Kong, Hong Kong, China

4. Ming-Hsieh Department of Electrical Engineering, Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, USA

Funder

National Natural Science Foundation of China

Shanghai Innovation Action Project of Science and Technology

Shanghai Municipal Science and Technology Major Project

Hong Kong Innovation and Technology Commission

Hong Kong GRF-RGC General Research Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Media Technology

Link

Reference54 articles.

5. CBAM: Convolutional block attention module;woo;Proc Eur Conf Comput Vis (ECCV),2018

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

3. Key Technologies on High-capacity Satellite Imagery Compression;2023 IEEE 11th International Conference on Information, Communication and Networks (ICICN);2023-08-17