Yu-Huan Qi

Cross-domain shadow detection via test-time augmentation

A test-time adaptation framework that requires no target-domain supervision that introduces two key innovations: a learnable dynamic augmentation module for better capturing target-domain characteristics, and a self-supervised adaptation strategy for leveraging unlabeled target-domain images by enforcing structural priors and cross-augmentation consistency, thereby improving prediction consistency under domain shift.