Deep learning algorithms are a subset of the machine learning algorithms, which aim at
discovering multiple levels of distributed representations. Recently, numerous deep learning …

Object detection is a fundamental but challenging issue in the field of generic image
analysis; it plays an important role in a wide range of applications and has been receiving …

Object detection is a well-known task in the field of computer vision, especially the small
target detection problem that has aroused great academic attention. In order to improve the …

Abstract Knowledge distillation is a widely used paradigm for inheriting information from a
complicated teacher network to a compact student network and maintaining the strong …

In this paper, we introduce a new large-scale object detection dataset, Objects365, which
has 365 object categories over 600K training images. More than 10 million, high-quality …

Estimating 3D bounding boxes from monocular images is an essential component in
autonomous driving, while accurate 3D object detection from this kind of data is very …

Progress on object detection is enabled by datasets that focus the research community's
attention on open challenges. This process led us from simple images to complex scenes …

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 …

Few-shot object detection is an imperative and long-lasting problem due to the inherent long-
tail distribution of real-world data. Its performance is largely affected by the data scarcity of …

Data privacy mechanisms are essential for rapidly scaling medical training databases to
capture the heterogeneity of patient data distributions toward robust and generalizable …