Object detection is a fundamental visual recognition problem in computer vision and has
been widely studied in the past decades. Visual object detection aims to find objects of …

Abstract Deep Neural Networks (DNNs) learn representation from data with an impressive
capability, and brought important breakthroughs for processing images, time-series, natural …

There are a huge number of features which are said to improve Convolutional Neural
Network (CNN) accuracy. Practical testing of combinations of such features on large …

Bounding box regression is the crucial step in object detection. In existing methods, while ℓ
n-norm loss is widely adopted for bounding box regression, it is not tailored to the evaluation …

We present a simple yet effective fully convolutional one-stage 3D object detector for LiDAR
point clouds of autonomous driving scenes, termed FCOS-LiDAR. Unlike the dominant …

One-stage object detection is commonly implemented by optimizing two sub-tasks: object
classification and localization, using heads with two parallel branches, which might lead to a …

The recently-developed DETR approach applies the transformer encoder and decoder
architecture to object detection and achieves promising performance. In this paper, we …

We present MMDetection, an object detection toolbox that contains a rich set of object
detection and instance segmentation methods as well as related components and modules …

Detection identifies objects as axis-aligned boxes in an image. Most successful object
detectors enumerate a nearly exhaustive list of potential object locations and classify each …

Object detection is a significant downstream task in computer vision. For the on-board edge
computing platforms, a giant model is difficult to achieve the real-time detection requirement …