MIC
Masked Image Consistency (MIC) advances unsupervised domain adaptation by focusing on spatial context relations in target domains. Through consistency between masked image predictions and pseudo-labels, MIC enhances visual recognition performance in tasks like image classification, semantic segmentation, and object detection. Suitable for various UDA challenges, including synthetic-to-real and clear-to-adverse-weather scenarios, MIC achieves high performance in benchmarks such as GTA to Cityscapes and VisDA-2017, contributing significantly to domain adaptation research.
