Data-driven decision-making in modern healthcare underpins innovation and predictive
analytics in public health and clinical research. Synthetic data has shown promise in finance …

Artificial intelligence (AI) models for medical imaging tasks, such as classification or
segmentation, require large and diverse datasets of images. However, due to privacy and …

The foundation models have recently shown excellent performance on a variety of
downstream tasks in computer vision. However, most existing vision foundation models …

Abstract Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of
machine learning systems. For instance, in autonomous driving, we would like the driving …

Supervised learning aims to train a classifier under the assumption that training and test
data are from the same distribution. To ease the above assumption, researchers have …

In this paper, we consider a highly general image recognition setting wherein, given a
labelled and unlabelled set of images, the task is to categorize all images in the unlabelled …

Despite the progress made by few-shot segmentation (FSS) in low-data regimes, the
generalization capability of most previous works could be fragile when countering hard …

Machine learning models often encounter samples that are diverged from the training
distribution. Failure to recognize an out-of-distribution (OOD) sample, and consequently …

Open set recognition (OSR), aiming to simultaneously classify the seen classes and identify
the unseen classes as' unknown', is essential for reliable machine learning. The key …

Real-world machine learning systems need to analyze novel testing data that differs from the
training data. In K-way classification, this is crisply formulated as open-set recognition, core …