Prototypical Contrast Adaptation for Domain Adaptive Semantic Segmentation
This work addresses domain shift in semantic segmentation for applications like autonomous driving, representing an incremental improvement by focusing on inter-class relationships.
The paper tackles the problem of unsupervised domain adaptation for semantic segmentation by introducing Prototypical Contrast Adaptation (ProCA), which incorporates inter-class structural relationships into class-wise prototypes to achieve class-centered distribution alignment, resulting in state-of-the-art performance on tasks like GTA5 to Cityscapes and SYNTHIA to Cityscapes.
Unsupervised Domain Adaptation (UDA) aims to adapt the model trained on the labeled source domain to an unlabeled target domain. In this paper, we present Prototypical Contrast Adaptation (ProCA), a simple and efficient contrastive learning method for unsupervised domain adaptive semantic segmentation. Previous domain adaptation methods merely consider the alignment of the intra-class representational distributions across various domains, while the inter-class structural relationship is insufficiently explored, resulting in the aligned representations on the target domain might not be as easily discriminated as done on the source domain anymore. Instead, ProCA incorporates inter-class information into class-wise prototypes, and adopts the class-centered distribution alignment for adaptation. By considering the same class prototypes as positives and other class prototypes as negatives to achieve class-centered distribution alignment, ProCA achieves state-of-the-art performance on classical domain adaptation tasks, {\em i.e., GTA5 $\to$ Cityscapes \text{and} SYNTHIA $\to$ Cityscapes}. Code is available at \href{https://github.com/jiangzhengkai/ProCA}{ProCA}