Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

Over the past decade, domain adaptation has become a widely studied branch of transfer
learning which aims to improve performance on target domains by leveraging knowledge …

Object detection (OD) is a crucial computer vision task that has seen the development of
many algorithms and models over the years. While the performance of current OD models …

Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention
for tackling domain-shift problems caused by distribution discrepancy across different …

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 …

In image fusion, images obtained from different sensors are fused to generate a single
image with enhanced information. In recent years, state-of-the-art methods have adopted …

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 …

Existing object detection models assume both the training and test data are sampled from
the same source domain. This assumption does not hold true when these detectors are …

Source-free object detection (SFOD) aims to adapt the source detector to unlabeled target
domain data in the absence of source domain data. Most SFOD methods follow the same …

Source-free object detection (SFOD) aims to transfer a detector pre-trained on a label-rich
source domain to an unlabeled target domain without seeing source data. While most …