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 …

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 …

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 …

Object detection in remote sensing images (RSIs) often suffers from several increasing
challenges including the large variation in object scales and the diverse-ranging context …

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 …

Large-scale vision foundation models have made significant progress in visual tasks on
natural images, with vision transformers (ViTs) being the primary choice due to their good …

Rotated object detection aims to identify and locate objects in images with arbitrary
orientation. In this scenario, the oriented directions of objects vary considerably across …

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 …

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

Detecting arbitrarily oriented tiny objects poses intense challenges to existing detectors,
especially for label assignment. Despite the exploration of adaptive label assignment in …