Addressing Sample Inconsistency for Semisupervised Object Detection in Remote Sensing Images

Addressing Sample Inconsistency for Semisupervised Object Detection in Remote Sensing Images

The emergence of semisupervised object detection (SSOD) techniques has greatly enhanced object detection performance. SSOD leverages a limited amount of labeled data along with a large quantity of unlabeled data. However, there exists a problem of sample inconsistency in remote sensing images, which...

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Bibliographic Details
Main Authors:	Yuhao Wang, Lifan Yao, Gang Meng, Xinye Zhang, Jiayun Song, Haopeng Zhang
Format:	Article
Language:	English
Published:	IEEE 2024-01-01
Series:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:	Active learning long-tailed distribution remote sensing semisupervised object detection (SSOD)
Online Access:	https://ieeexplore.ieee.org/document/10463140/

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