Hybridizing Euclidean and Hyperbolic Similarities for Attentively Refining Representations in Semantic Segmentation of Remote Sensing Images-Reference-Cited by-同舟云学术

Hybridizing Euclidean and Hyperbolic Similarities for Attentively Refining Representations in Semantic Segmentation of Remote Sensing Images

Published:2022 Issue: Volume:19 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Li Xin¹^ORCID,Xu Feng¹^ORCID,Liu Fan¹,Xia Runliang²^ORCID,Tong Yao³^ORCID,Li Linyang⁴,Xu Zhennan¹^ORCID,Lyu Xin¹^ORCID

Affiliation:

1. College of Computer and Information and the Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing, China

2. Information Engineering Center, Yellow River Institute of Hydraulic Research, Zhengzhou, China

3. School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China

4. Surveying and Mapping Institute, PLA Information Engineering University, Zhengzhou, China

Funder

Excellent Post-Doctoral Program of Jiangsu Province

National Natural Science Foundation of China

Project of Water Science and Technology of Jiangsu Province

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology

Link

http://xplorestaging.ieee.org/ielx7/8859/9651998/09966596.pdf?arnumber=9966596

Reference18 articles.

1. Hyperbolic deep neural networks: A survey;peng;arXiv 2101 04562,2021

2. Geometric Deep Learning: Going beyond Euclidean data

3. Hyperbolic neural networks;ganea;Proc Adv Neural Inf Process Syst,2018

4. Hyperbolic Visual Embedding Learning for Zero-Shot Recognition

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