The loss function, also known as cost function, is used for training a neural network or other
machine learning models. Over the past decade, researchers have designed many loss …

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 …

Current state-of-the-art two-stage detectors generate oriented proposals through time-
consuming schemes. This diminishes the detectors' speed, thereby becoming the …

Recent research on remote sensing object detection has largely focused on improving the
representation of oriented bounding boxes but has overlooked the unique prior knowledge …

In this paper, we aim to design an efficient real-time object detector that exceeds the YOLO
series and is easily extensible for many object recognition tasks such as instance …

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 …

We present an open-source toolbox, named MMRotate, which provides a coherent algorithm
framework of training, inferring, and evaluation for the popular rotated object detection …

Existing rotated object detectors are mostly inherited from the horizontal detection paradigm,
as the latter has evolved into a well-developed area. However, these detectors are difficult to …

Boundary discontinuity and its inconsistency to the final detection metric have been the
bottleneck for rotating detection regression loss design. In this paper, we propose a novel …

Three-dimensional objects are commonly represented as 3D boxes in a point-cloud. This
representation mimics the well-studied image-based 2D bounding-box detection but comes …