Open-world classification systems should discern out-of-distribution (OOD) data whose
labels deviate from those of in-distribution (ID) cases, motivating recent studies in OOD …

Unsupervised domain adaptation (UDA) addresses the challenge of distribution shift
between a labeled source domain and an unlabeled target domain by utilizing knowledge …

This article provides a novel framework for single-domain generalized object detection (ie,
Single-DGOD), where we are interested in learning and maintaining the semantic structures …

Unsupervised domain adaptation (UDA) person re-identification (Re-ID) is to enhance the
discriminability of Re-ID tasks in the target domain by leveraging labeled source domain …

Out-of-distribution (OOD) generalization is critical for machine learning models deployed in
the real world. However, achieving this can be fundamentally challenging, as it requires the …

Domain generalization aims to learn a well-performed classifier on multiple source domains
for unseen target domains under domain shift. Domain-invariant representation (DIR) is an …

Abstract Out-of-distribution (OOD) detection is crucial to modern deep learning applications
by identifying and alerting about the OOD samples that should not be tested or used for …

Disease grading is a crucial task in medical image analysis. Due to the continuous
progression of diseases, ie, the variability within the same level and the similarity between …

The emerging vision foundation model (VFM) has inherited the ability to generalize to
unseen images. Nevertheless, the key challenge of domain-generalized semantic …

Predominant instrument recognition plays a vital role in music information retrieval. This task
involves identifying and categorizing the dominant instruments present in a piece of music …