Deep learning has been overwhelmingly successful in computer vision (CV), natural
language processing, and video/speech recognition. In this paper, our focus is on CV. We …

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 …

For years, the YOLO series has been the de facto industry-level standard for efficient object
detection. The YOLO community has prospered overwhelmingly to enrich its use in a …

We propose DiffusionDet, a new framework that formulates object detection as a denoising
diffusion process from noisy boxes to object boxes. During the training stage, object boxes …

With the rise of deep convolutional neural networks, object detection has achieved
prominent advances in past years. However, such prosperity could not camouflage the …

Object detection on drone-captured scenarios is a recent popular task. As drones always
navigate in different altitudes, the object scale varies violently, which burdens the …

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

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 …

The complex nature of combining localization and classification in object detection has
resulted in the flourished development of methods. Previous works tried to improve the …

Previous pseudo-label approaches for semi-supervised object detection typically follow a
multi-stage schema, with the first stage to train an initial detector on a few labeled data …