Deep neural networks (DNNs) have revolutionized artificial intelligence but often lack
performance when faced with out-of-distribution data, a common scenario due to the …

Single-domain generalization aims to learn a model from single source domain data
attaining generalized performance on other unseen target domains. Existing works primarily …

Abstract Domain Generalization (DG) aims to develop a versatile model capable of
performing well on unseen target domains. Recent advancements in pre-trained Visual …

Video domain generalization aims to learn generalizable video classification models for
unseen target domains by training in a source domain. A critical challenge of video domain …

Existing hyperspectral cross-domain few-shot learning (FSL) methods focus mainly on
elaborating on training strategies or domain alignment algorithms, while paying less …

Deep learning has revolutionized the field of artificial intelligence. Based on the statistical
correlations uncovered by deep learning-based methods, computer vision tasks, such as …

Single-domain generalization (SDG) fault diagnosis methods are promising approach
because they can diagnose unknown domains by training only one domain. However, there …

Non-transferable learning (NTL) aims to restrict the generalization of models toward the
target domain (s). To this end, existing works learn non-transferable representations by …

Single-source domain generalization (SDG) for object detection is a challenging yet
essential task as the distribution bias of the unseen domain degrades the algorithm …

Image-text matching bridges vision and language, which is a fundamental task in multimodal
intelligence. Its key challenge lies in how to capture visual-semantic relevance. Fine-grained …