  Existing shadow detection datasets often contain missing or mislabeled
shadows, which can hinder the performance of deep learning models trained
directly on such data. To address this issue, we propose SILT, the Shadow-aware
Iterative Label Tuning framework, which explicitly considers noise in shadow
labels and trains the deep model in a self-training manner. Specifically, we
incorporate strong data augmentations with shadow counterfeiting to help the
network better recognize non-shadow regions and alleviate overfitting. We also
devise a simple yet effective label tuning strategy with global-local fusion
and shadow-aware filtering to encourage the network to make significant
refinements on the noisy labels. We evaluate the performance of SILT by
relabeling the test set of the SBU dataset and conducting various experiments.
Our results show that even a simple U-Net trained with SILT can outperform all
state-of-the-art methods by a large margin. When trained on SBU / UCF / ISTD,
our network can successfully reduce the Balanced Error Rate by 25.2% / 36.9% /
21.3% over the best state-of-the-art method.
