WCT-Enhanced Instance Normalization for Unsupervised Domain Adaptation in Object Detection

Abstract

Safety-critical applications like video surveillance, traffic monitoring, and autonomous driving often face a lack of labeled data and performance degradation due to domain shifts between training and testing sets. Unsupervised Domain Adaptation (UDA) offers a viable solution when annotating the target domain is costly and time consuming. We propose a novel WCT-IN stylization module that injects Gaussian noise into the style feature representation, combining Adaptive Instance Normalization (AdaIN) for style transfer with Whitening and Coloring Transform (WCT) for color correction and feature transformation. This generates intermediate images that bridge the source-target domain gap while preserving source labels. Integrated with a consistency learning framework, our method improves adaptation performance. Extensive experiments on benchmark datasets (Cityscapes, Foggy Cityscapes, Sim10K, and KITTI) demonstrate the effectiveness of our approach in enhancing object detection under domain shifts.