A Semi-Supervised Object Detector Based on Adaptive Weighted Active Learning and Orthogonal Data Augmentation

A Semi-Supervised Object Detector Based on Adaptive Weighted Active Learning and Orthogonal Data Augmentation

To efficiently utilize limited resources, this paper proposes a semi-supervised object detection (SSOD) approach based on novel adaptive weighted active learning (AWAL) and orthogonal data augmentation (ODA). An uncertainty sampling framework is applied by adaptively weighting multiple evaluations t...

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Bibliographic Details
Main Authors: Meng Wang, Xiao Xu, Haipeng Liu
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Sensors
Subjects:
semi-supervised learning
active learning
contrastive learning
object detection
unlabeled data mining
Online Access:https://www.mdpi.com/1424-8220/25/6/1798
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