In this paper, we delve into semi-supervised object detection where unlabeled images are
leveraged to break through the upper bound of fully-supervised object detection models …

In this paper, we focus on semi-supervised object detection to boost performance of
proposal-based object detectors (aka two-stage object detectors) by training on both labeled …

Object detection (OD) coupled with active learning (AL) has emerged as a powerful synergy
in the field of computer vision, harnessing the capabilities of machine learning (ML) to …

Active learning seeks to achieve strong performance with fewer training samples. It does this
by iteratively asking an oracle to label newly selected samples in a human-in-the-loop …

Limited by the GPU memory, the current mainstream detectors fail to directly apply to large-
scale remote sensing images for object detection. Moreover, the scale range of objects in …

In this paper, an unsupervised co-segmentation algorithm is proposed, which can be
applied to the image with multiple foreground objects simultaneously and the background …

Because of the development of image processing using cameras and the subsequent
development of artificial intelligence technology, various fields have begun to develop …

Object detection in streaming images is a major step in different detection-based
applications, such as object tracking, action recognition, robot navigation, and visual …

Recently, CNN object detectors have achieved high accuracy on remote sensing images but
require huge labor and time costs on annotation. In this paper, we propose a new …

Object detection is one of the major areas of computer vision, which adopts machine
learning approaches in diverse contributions. Nowadays, the machine learning field has …