Abstract Unsupervised Domain Adaptation (UDA) for object detection aims to adapt a model
trained on a source domain to detect instances from a new target domain for which …

Compared to unsupervised domain adaptation, semi-supervised domain adaptation (SSDA)
aims to significantly improve the classification performance and generalization capability of …

Abstract Domain adaptation (DA) aims to transfer knowledge from a label-rich source
domain to a related but label-scarce target domain. Recently, increasing research has …

Anomaly detection in videos is a significant yet challenging problem. Previous approaches
based on deep neural networks employ either reconstruction-based or prediction-based …

Despite plenty of efforts focusing on improving the domain adaptation ability (DA) under
unsupervised or few-shot semi-supervised settings, recently the solution of active learning …

Semi-supervised domain adaptation (SSDA) aims to apply knowledge learned from a fully
labeled source domain to a scarcely labeled target domain. In this paper, we propose a Multi …

Source-free domain adaptation (SFDA) aims to adapt a well-trained source model to an
unlabelled target domain without accessing the source dataset, making it applicable in a …

In medical image analysis, we often need to build an image recognition system for a target
scenario with the access to small labeled data and abundant unlabeled data, as well as …

Source-free domain adaptation (SFDA) is an emerging research topic that studies how to
adapt a pretrained source model using unlabeled target data. It is derived from …

The bi-classifier paradigm is a common practice in unsupervised domain adaptation (UDA),
where two classifiers are leveraged to guide the model to learn domain invariant features …