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Unsupervised Domain Adaptive Object Detection via Semantic Consistency and Compactness Learning.

Yajing Liu, Zhen Zhang, Yiming Su

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 19, 2026

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This study introduces a new Semantic Consistency and Compactness Learning (SCCL) network for unsupervised domain adaptive object detection. SCCL improves feature consistency and category compactness, enhancing model robustness without target-domain annotations.

Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Unsupervised domain adaptive object detection aims to improve model robustness in new domains without labeled data.
Existing methods struggle with holistic feature consistency and reliable category feature compactness.
Challenges include inefficient style matching, semantic discrepancy, poor sample quality, and noisy contrastive learning.

Purpose of the Study:

To propose a novel Semantic Consistency and Compactness Learning (SCCL) network.
To address the limitations of insufficient/inefficient consistency learning and unreliable compactness learning in unsupervised domain adaptation.
To enhance feature transferability and discriminability for robust object detection.

Main Methods:

Introduced a Visual Adaptation-guided Semantic Alignment (VSA) module for efficient feature consistency learning via feature adaptation and adversarial-free self-supervised feature disentanglement.

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Developed a plug-and-play Instance Center-Contrastive (ICC) head to address unreliable compactness learning by enhancing pseudo-label quality, improving sample storage/updating, and refining the contrast paradigm.

Leveraged the mutual reinforcement between VSA and ICC.

Main Results:

The proposed SCCL network demonstrated superior adaptability and robustness in unsupervised domain adaptive object detection.
Achieved significant improvements across four benchmark datasets.
The VSA and ICC modules effectively enhanced feature transferability and discriminability.

Conclusions:

The SCCL network effectively overcomes key challenges in unsupervised domain adaptive object detection.
The VSA and ICC modules provide a robust framework for learning feature consistency and compactness.
SCCL offers a promising approach for enhancing model robustness in diverse target domains.