Distilling Knowledge From Super-Resolution for Efficient Remote Sensing Salient Object Detection-Reference-Cited by-同舟云学术

Distilling Knowledge From Super-Resolution for Efficient Remote Sensing Salient Object Detection

Published:2023 Issue: Volume:61 Page:1-16
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Liu Yanfeng¹^ORCID,Xiong Zhitong²^ORCID,Yuan Yuan²^ORCID,Wang Qi³^ORCID

Affiliation:

1. School of Computer Science and the School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, China

2. Chair of Data Science in Earth Observation, Technical University of Munich (TUM), Munich, Germany

3. School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10102592.pdf?arnumber=10102592

Reference74 articles.

1. A Simple Pooling-Based Design for Real-Time Salient Object Detection

2. Super-Resolution Integrated Building Semantic Segmentation for Multi-Source Remote Sensing Imagery

3. Reverse Attention for Salient Object Detection

4. Feature Pyramid Networks for Object Detection

5. SAMNet: Stereoscopically Attentive Multi-Scale Network for Lightweight Salient Object Detection

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