Remote sensing image classification is pivotal in applications ranging from environmental
monitoring to urban planning. However, the scarcity of labeled data in target domains often …

Deep learning methods can learn effective representations from the data, simplifying the
fault diagnosis process and improving accuracy. However, the lack of data presents a …

The acquisition of large-scale, precisely labeled datasets for person re-identification (ReID)
poses a significant challenge. Weakly supervised ReID has begun to address this issue …

Unsupervised domain adaptation (UDA) methods have recently been explored for their use
in Skeleton recognition tasks. Much work along this line has been focusing on the “close-set” …

In open-set recognition, existing methods generally learn statically fixed decision
boundaries using known classes to reject unknown classes. Though they have achieved …

Background Wide-field calcium imaging (WFCI) with genetically encoded calcium indicators
allows for spatiotemporal recordings of neuronal activity in mice. When applied to the study …

The challenge of bias in visual question answering (VQA) has gained considerable attention
in contemporary research. Various intricate bias dependencies, such as modalities and data …

Deep learning, despite its broad applicability, grapples with robustness challenges in real-
world applications, especially when training and test distributions differ. Reasons for the …

Machine learning models can fail when trained on distributions with hidden confounders
(spuriously correlated with the label) and tested on distributions where such correlations are …