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...
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: | |
Online Access: | https://ieeexplore.ieee.org/document/10463140/ |
Similar Items
-
FMWDCT: Foreground Mixup Into Weighted Dual-Network Cross Training for Semisupervised Remote Sensing Road Extraction
by: Zhi-Hui You, et al.
Published: (2022-01-01) -
Semisupervised Semantic Segmentation With Certainty-Aware Consistency Training for Remote Sensing Imagery
by: Yongjie Guo, et al.
Published: (2023-01-01) -
A Cotraining-Based Semisupervised Approach for Remaining-Useful-Life Prediction of Bearings
by: Xuguo Yan, et al.
Published: (2022-10-01) -
Semisupervised Cross Domain Teacher–Student Mutual Training for Damaged Building Detection
by: Jie Pan, et al.
Published: (2023-01-01) -
Cluster2Former: Semisupervised Clustering Transformers for Video Instance Segmentation
by: Áron Fóthi, et al.
Published: (2024-02-01)