Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …

In unsupervised domain adaptation (UDA), a model trained on source data (eg synthetic) is
adapted to target data (eg real-world) without access to target annotation. Most previous …

U-Net has become a cornerstone in various visual applications such as image segmentation
and diffusion probability models. While numerous innovative designs and improvements …

Object detectors often suffer from the domain gap between training (source domain) and real-
world applications (target domain). Mean-teacher self-training is a powerful paradigm in …

Recent advances in deep learning have led to the development of accurate and efficient
models for various computer vision applications such as classification, segmentation, and …

Most object detection methods for autonomous driving usually assume a onsistent feature
distribution between training and testing data, which is not always the case when weathers …

Self-training approaches recently achieved promising results in cross-domain object
detection, where people iteratively generate pseudo labels for unlabeled target domain …

Recent advances in learning multi-modal representation have witnessed the success in
biomedical domains. While established techniques enable handling multi-modal …

Abstract Domain adaptive object detection (DAOD) aims to alleviate transfer performance
degradation caused by the cross-domain discrepancy. However, most existing DAOD …

Abstract Open Set Domain Adaptation (OSDA) transfers the model from a label-rich domain
to a label-free one containing novel-class samples. Existing OSDA works overlook abundant …