Reconstructing Domain-Specific Features for Unsupervised Domain-Adaptive Object Detection

Unsupervised domain adaptation (UDA) effectively transfers knowledge learned from a labeled source domain to an unlabeled target domain. The teacher–student framework, which generates pseudo-labels for target domain samples and uses them for pseudo-supervised training, enables self-training and impr...

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Bibliographic Details
Main Authors:	Shuai Dong, Kang Deng, Kun Zou
Format:	Article
Language:	English
Published:	MDPI AG 2025-05-01
Series:	Information
Subjects:	one-stage object detection domain adaptation domain-specific head teacher–student framework feature reconstruction unreliable pseudo-labels
Online Access:	https://www.mdpi.com/2078-2489/16/6/439
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Reconstructing Domain-Specific Features for Unsupervised Domain-Adaptive Object Detection

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