Deep learning has received extensive research interest in developing new medical image
processing algorithms, and deep learning based models have been remarkably successful …

In computer vision, significant advances have been made on object detection with the rapid
development of deep convolutional neural networks (CNN). This paper provides a …

High-resolution representations are essential for position-sensitive vision problems, such as
human pose estimation, semantic segmentation, and object detection. Existing state-of-the …

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 …

Object detection is a fundamental visual recognition problem in computer vision and has
been widely studied in the past decades. Visual object detection aims to find objects of …

In object detection, keypoint-based approaches often experience the drawback of a large
number of incorrect object bounding boxes, arguably due to the lack of an additional …

Modern object detectors rely heavily on rectangular bounding boxes, such as anchors,
proposals and the final predictions, to represent objects at various recognition stages. The …

With the rapid development of deep learning techniques, deep convolutional neural
networks (DCNNs) have become more important for object detection. Compared with …

High-resolution representation learning plays an essential role in many vision problems, eg,
pose estimation and semantic segmentation. The high-resolution network (HRNet)~\cite …

With the advent of deep learning, object detection drifted from a bottom-up to a top-down
recognition problem. State of the art algorithms enumerate a near-exhaustive list of object …