Open Compound Domain Adaptation with Object Style Compensation for Semantic Segmentation
This work addresses the challenge of adapting object styles across domains for semantic segmentation, which is an incremental improvement over existing global scene adaptation methods.
The paper tackled the problem of improper object style adaptation in open compound domain adaptation for semantic segmentation by proposing Object Style Compensation with an Object-Level Discrepancy Memory, resulting in state-of-the-art performance on various datasets.
Many methods of semantic image segmentation have borrowed the success of open compound domain adaptation. They minimize the style gap between the images of source and target domains, more easily predicting the accurate pseudo annotations for target domain's images that train segmentation network. The existing methods globally adapt the scene style of the images, whereas the object styles of different categories or instances are adapted improperly. This paper proposes the Object Style Compensation, where we construct the Object-Level Discrepancy Memory with multiple sets of discrepancy features. The discrepancy features in a set capture the style changes of the same category's object instances adapted from target to source domains. We learn the discrepancy features from the images of source and target domains, storing the discrepancy features in memory. With this memory, we select appropriate discrepancy features for compensating the style information of the object instances of various categories, adapting the object styles to a unified style of source domain. Our method enables a more accurate computation of the pseudo annotations for target domain's images, thus yielding state-of-the-art results on different datasets.