Regression-Guided Refocusing Learning With Feature Alignment for Remote Sensing Tiny Object Detection-Reference-Cited by-同舟云学术

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Regression-Guided Refocusing Learning With Feature Alignment for Remote Sensing Tiny Object Detection

Published:2024 Issue: Volume:62 Page:1-14
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Ge Lihui¹^ORCID,Wang Guanqun¹^ORCID,Zhang Tong¹^ORCID,Zhuang Yin¹^ORCID,Chen He¹^ORCID,Dong Hao²^ORCID,Chen Liang¹^ORCID

Affiliation:

1. National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing, Beijing Institute of Technology, Beijing, China

2. Center on Frontiers of Computing Studies (CFCS), Peking University, Beijing, China

Funder

General Program of the National Natural Science Foundation of China

National Science Foundation for Young Scientists of China

Multisource Satellite Data Hardware Acceleration Computing Method With Low Energy Consumption

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/36/10354519/10542513.pdf?arnumber=10542513

Reference60 articles.

1. A Survey of the Four Pillars for Small Object Detection: Multiscale Representation, Contextual Information, Super-Resolution, and Region Proposal

2. The 1st Tiny Object Detection Challenge: Methods and Results

3. Faster R-CNN: Towards real-time object detection with region proposal networks;Ren

4. Mask R-CNN

5. Cascade R-CNN: Delving Into High Quality Object Detection

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