Object detection, as of one the most fundamental and challenging problems in computer
vision, has received great attention in recent years. Over the past two decades, we have …

3D object detection from images, one of the fundamental and challenging problems in
autonomous driving, has received increasing attention from both industry and academia in …

Recent advances in label assignment in object detection mainly seek to independently
define positive/negative training samples for each ground-truth (gt) object. In this paper, we …

Deep learning-based object detection and instance segmentation have achieved
unprecedented progress. In this article, we propose complete-IoU (CIoU) loss and Cluster …

Bounding box regression is the crucial step in object detection. In existing methods, while ℓ
n-norm loss is widely adopted for bounding box regression, it is not tailored to the evaluation …

Object detection is one of the most important and challenging branches of computer vision,
which has been widely applied in people's life, such as monitoring security, autonomous …

Mainstream object detectors based on the fully convolutional network has achieved
impressive performance. While most of them still need a hand-designed non-maximum …

The crowded scenario not only contains instances at various scales but also introduces a
variety of occlusion patterns ranging from non-occluded situations to heavily occluded …

Can our video understanding systems perceive objects when a heavy occlusion exists in a
scene? To answer this question, we collect a large-scale dataset called OVIS for occluded …

We propose a simple yet effective proposal-based object detector, aiming at detecting highly-
overlapped instances in crowded scenes. The key of our approach is to let each proposal …