Radiomics is an emerging area in quantitative image analysis that aims to relate large‐scale
extracted imaging information to clinical and biological endpoints. The development of …

The aim of this paper is to give an overview of domain adaptation and transfer learning with
a specific view on visual applications. After a general motivation, we first position domain …

In this work, we revisit the weak-to-strong consistency framework, popularized by FixMatch
from semi-supervised classification, where the prediction of a weakly perturbed image …

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 …

We propose a pre-training strategy called Multi-modal Multi-task Masked Autoencoders
(MultiMAE). It differs from standard Masked Autoencoding in two key aspects: I) it can …

Self-training via pseudo labeling is a conventional, simple, and popular pipeline to leverage
unlabeled data. In this work, we first construct a strong baseline of self-training (namely ST) …

While semi-supervised learning (SSL) has received tremendous attentions in many machine
learning tasks due to its successful use of unlabeled data, existing SSL algorithms use either …

Semantic segmentation labels are expensive and time consuming to acquire. To improve
label efficiency of semantic segmentation models, we revisit denoising autoencoders and …

Pre-training is a dominant paradigm in computer vision. For example, supervised ImageNet
pre-training is commonly used to initialize the backbones of object detection and …

Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data
to improve a model's performance. This domain has seen fast progress recently, at the cost …