Introduction: There has been a rapid development of deep learning (DL) models for medical
imaging. However, DL requires a large labeled dataset for training the models. Getting large …

Breast cancer has reached the highest incidence rate worldwide among all malignancies
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …

Deep long-tailed learning, one of the most challenging problems in visual recognition, aims
to train well-performing deep models from a large number of images that follow a long-tailed …

Deep neural networks have exhibited promising performance in image super-resolution
(SR) by learning a nonlinear mapping function from low-resolution (LR) images to high …

We study a practical domain adaptation task, called source-free unsupervised domain
adaptation (UDA) problem, in which we cannot access source domain data due to data …

We address the problem of video grounding from natural language queries. The key
challenge in this task is that one training video might only contain a few annotated …

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 …

Unsupervised domain adaptation (UDA) aims to exploit the knowledge learned from a
labeled source dataset to solve similar tasks in a new unlabeled target domain. Existing …

Deep learning based medical image diagnosis has shown great potential in clinical
medicine. However, it often suffers two major difficulties in real-world applications: 1) only …

This paper studies a new, practical but challenging problem, called Class-Incremental
Unsupervised Domain Adaptation (CI-UDA), where the labeled source domain contains all …