We address the problem of Multi-Source Domain Adaptation (MSDA), which trains a neural
network using multiple labeled source datasets and an unlabeled target dataset, and …

Abstract Domain Generalization-based Medical Image Segmentation (DGMIS) aims to
enhance the robustness of segmentation models on unseen target domains by learning from …

Abstract Domain generalization learns from one or multiple source domains. It aims to
extract a domain-invariant model that can be employed in an unknown target domain …

Establishing the relationships among hierarchical visual attributes of objects in the visual
world is crucial for human cognition. The classic convolution neural network (CNN) can …

This article addresses the challenge of adapting deep learning models trained on specific
datasets to effectively generalize to similar-class dataset with different underlying …

Deep neural networks often suffer performance degradation when the testing data
distribution differs significantly from the training data distribution. To address this problem …

Abstract Domain generalized semantic segmentation is an essential computer vision task,
for which models only leverage source data to learn semantic segmentation towards …