PAGen: Phase-guided Amplitude Generation for Domain-adaptive Object Detection

• URL: http://arxiv.org/abs/2511.22029v1
• Date: Thu, 27 Nov 2025 02:22:37 GMT
• Title: PAGen: Phase-guided Amplitude Generation for Domain-adaptive Object Detection
• Authors: Shuchen Du, Shuo Lei, Feiran Li, Jiacheng Li, Daisuke Iso,
• Abstract summary: Unsupervised domain adaptation (UDA) greatly facilitates the deployment of neural networks across diverse environments.<n>We present a simple yet effective UDA method that learns to adapt image styles in the frequency domain to reduce the discrepancy between source and target domains.
• Score: 15.55359477953804
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Unsupervised domain adaptation (UDA) greatly facilitates the deployment of neural networks across diverse environments. However, most state-of-the-art approaches are overly complex, relying on challenging adversarial training strategies, or on elaborate architectural designs with auxiliary models for feature distillation and pseudo-label generation. In this work, we present a simple yet effective UDA method that learns to adapt image styles in the frequency domain to reduce the discrepancy between source and target domains. The proposed approach introduces only a lightweight pre-processing module during training and entirely discards it at inference time, thus incurring no additional computational overhead. We validate our method on domain-adaptive object detection (DAOD) tasks, where ground-truth annotations are easily accessible in source domains (e.g., normal-weather or synthetic conditions) but challenging to obtain in target domains (e.g., adverse weather or low-light scenes). Extensive experiments demonstrate that our method achieves substantial performance gains on multiple benchmarks, highlighting its practicality and effectiveness.

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