Advances in computing technology have fostered the development of new and powerful
deep learning (DL) techniques, which have demonstrated promising results in a wide range …

To better understand early brain development in health and disorder, it is critical to
accurately segment infant brain magnetic resonance (MR) images into white matter (WM) …

Traditional machine learning paradigms are based on the assumption that both training and
test data follow the same statistical pattern, which is mathematically referred to as …

In this paper, we explore the role of Instance Normalization in low-level vision tasks.
Specifically, we present a novel block: Half Instance Normalization Block (HIN Block), to …

Machine learning systems generally assume that the training and testing distributions are
the same. To this end, a key requirement is to develop models that can generalize to unseen …

Occluded person re-identification (ReID) is a person retrieval task which aims at matching
occluded person images with holistic ones. For addressing occluded ReID, part-based …

Enhancing the generalization capability of deep neural networks to unseen domains is
crucial for safety-critical applications in the real world such as autonomous driving. To …

Abstract Domain adaptive object re-ID aims to transfer the learned knowledge from the
labeled source domain to the unlabeled target domain to tackle the open-class re …

Deep models trained on source domain lack generalization when evaluated on unseen
target domains with different data distributions. The problem becomes even more …

Abstract Domain generalization aims to learn a generalizable model from aknown'source
domain for variousunknown'target domains. It has been studied widely by domain …