Advancements in deep learning and computer vision provide promising solutions for
medical image analysis, potentially improving healthcare and patient outcomes. However …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer
vision and pattern recognition, and underpins a diverse set of real-world applications. The …

Self-supervised learning (SSL) is a scalable way to learn general visual representations
since it learns without labels. However, large-scale unlabeled datasets in the wild often have …

Machine learning systems deployed in the wild are often trained on a source distribution but
deployed on a different target distribution. Unlabeled data can be a powerful point of …

Self-supervised pretraining bears the potential to generate expressive representations from
large-scale Earth observation (EO) data without human annotation. However, most existing …

Images of the natural world collected by a variety of cameras from drones to individual
phones are increasingly abundant sources of biological information. There is an explosion …

Abstract Vision Transformers (ViTs) have gained significant popularity in recent years and
have proliferated into many applications. However, their behavior under different learning …

Out-of-distribution (OOD) detection aims to detect “unknown” data whose labels have not
been seen during the in-distribution (ID) training process. Recent progress in representation …

Empowered by large datasets, eg, ImageNet and MS COCO, unsupervised learning on
large-scale data has enabled significant advances for classification tasks. However, whether …