5 accepted papers
Currently, almost all traditional infrared small target detection methods work on the assumption that training and test sets always belong to the same domain, and training samples are sufficient. However, in real applications, a new detection task could often have no sufficient training samples from
Different from traditional object detection, pure vision is not enough to infrared small target detection, due to small target size and weak background contrast. For promoting detection performance, more target representations are needed. Currently, motion representations have been proved to be one…
Sample shortage has always been a frequently-faced problem for the machine-learning models in infrared small target detection. As one of main limitations, it is hampering the further promotion of target detection performance. In this paper, we propose a simple and effective data augmentation scheme,…
Infrared small target detection has always been a challenging theme, due to small target size, unconspicuous contour and texture, even low vision contrast to background. Because of these causes, some popular object detection methods, such as Faster-RCNN and YOLOV, could often lose effectiveness. Aim…
The convolution operation suffers from a limited receptive field, while global modeling is fundamental to dense prediction tasks, such as semantic segmentation. However, most existing methods treat the recognition of each region separately and overlook crucial global semantic relations between regio…