Although deep learning has achieved satisfactory performance in computer vision, a large
volume of images is required. However, collecting images is often expensive and …

To ensure good performance, modern machine learning models typically require large
amounts of quality annotated data. Meanwhile, the data collection and annotation processes …

The great success of machine learning with massive amounts of data comes at a price of
huge computation costs and storage for training and tuning. Recent studies on dataset …

Conventional domain adaptation (DA) techniques aim to improve domain transferability by
learning domain-invariant representations; while concurrently preserving the task …

Improving the generalization of deep networks is an important open challenge, particularly
in domains without plentiful data. The mixup algorithm improves generalization by linearly …

Effective semi-supervised learning (SSL) in medical image analysis (MIA) must address two
challenges: 1) work effectively on both multi-class (eg, lesion classification) and multi-label …

Long-tailed data is still a big challenge for deep neural networks, even though they have
achieved great success on balanced data. We observe that vanilla training on long-tailed …

CutMix is a popular augmentation technique commonly used for training modern
convolutional and transformer vision networks. It was originally designed to encourage …

Curating a large set of fully annotated training data can be costly, especially for the tasks of
medical image segmentation. Scribble, a weaker form of annotation, is more obtainable in …

We consider a class-incremental semantic segmentation (CISS) problem. While some
recently proposed algorithms utilized variants of knowledge distillation (KD) technique to …