Visual segmentation seeks to partition images, video frames, or point clouds into multiple
segments or groups. This technique has numerous real-world applications, such as …

Deep learning has largely reshaped remote sensing (RS) research for aerial image
understanding and made a great success. Nevertheless, most of the existing deep models …

Image dehazing is always a hot topic in the field of computer vision since haze has
significant impact on the imaging quality of camera. Therefore, many image dehazing …

With the development of convolutional neural networks, hundreds of deep learning–based
dehazing methods have been proposed. In this article, we provide a comprehensive survey …

Cross domain object detection is a realistic and challenging task in the wild. It suffers from
performance degradation due to large shift of data distributions and lack of instance-level …

The recent detection transformer (DETR) simplifies the object detection pipeline by removing
hand-crafted designs and hyperparameters as employed in conventional two-stage object …

Infrared small target detection (IRSTD) refers to segmenting the small targets from infrared
images, which is of great significance in practical applications. However, due to the small …

Abstract Recently, DEtection TRansformer (DETR), an end-to-end object detection pipeline,
has achieved promising performance. However, it requires large-scale labeled data and …

Hyperspectral image (HSI) classification is challenging due to spatial variability caused by
complex imaging conditions. Prior methods suffer from limited representation ability, as they …

Abstract Domain adaptive Object Detection (DAOD) leverages a labeled domain (source) to
learn an object detector generalizing to a novel domain without annotation (target). Recent …