Domain-Adaptive Object Detection via Pseudo-Label Self-Training and Depth Priors
Unsupervised Domain Adaptation (UDA) for object detection remains challenging under adverse weather due to significant distribution shifts. While recent Vision Foundation Model (VFM) based methods show promise, they often encounter limitations in extreme domain gaps and pseudo-label noise. This paper proposes two enhancements to the DINO Teacher framework: (1) a multi-round self-training strategy to refine the labeling model progressively, and (2) a depth-guided spatial modulation mechanism using geometric priors from a DINOv3based depth estimator. By modulating the input space, the student model is encouraged to emphasize spatial cues that are less sensitive to visibility degradation in foggy environments. Experiments on Foggy Cityscapes demonstrate that our approach reaches 56.5% mAP with a VGG-16 backbone and 59.8% mAP with ResNet-50. These results demonstrate competitive performance compared with the DINO Teacher baseline and recent Vision Language Model (VLM) based methods, particularly for tail categories such as bus and train.