With the rapid development of object detection, it is widely used in many scenes and
images. However, the dense arrangement of objects with different dimensions, orientations …

With the rise and development of deep learning, computer vision has been tremendously
transformed and reshaped. As an important research area in computer vision, scene text …

Recently, object detection in aerial images has gained much attention in computer vision.
Different from objects in natural images, aerial objects are often distributed with arbitrary …

In he past decade, object detection has achieved significant progress in natural images but
not in aerial images, due to the massive variations in the scale and orientation of objects …

Owing to effective and flexible data acquisition, unmanned aerial vehicles (UAVs) have
recently become a hotspot across the fields of computer vision (CV) and remote sensing …

The past decade has witnessed significant progress on detecting objects in aerial images
that are often distributed with large-scale variations and arbitrary orientations. However …

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 …

Detecting oriented and densely packed objects remains challenging for spatial feature
aliasing caused by the intersection of reception fields between objects. In this paper, we …

Object detection, one of the most fundamental and challenging problems in computer vision,
seeks to locate object instances from a large number of predefined categories in natural …

The translation equivariance of convolutional layers enables CNNs to generalize well on
image problems. While translation equivariance provides a powerful inductive bias for …