In computer vision, significant advances have been made on object detection with the rapid
development of deep convolutional neural networks (CNN). This paper provides a …

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 …

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 …

Object detection has been dominated by anchor-based detectors for several years.
Recently, anchor-free detectors have become popular due to the proposal of FPN and Focal …

Accurately ranking the vast number of candidate detections is crucial for dense object
detectors to achieve high performance. Prior work uses the classification score or a …

Recent advances in label assignment in object detection mainly seek to independently
define positive/negative training samples for each ground-truth (gt) object. In this paper, we …

The past decade has witnessed significant progress on detecting objects in aerial images
that are often distributed with large-scale variations and arbitrary orientations. However …

Object detection is one of the most important and challenging branches of computer vision,
which has been widely applied in people's life, such as monitoring security, autonomous …

Cascade is a classic yet powerful architecture that has boosted performance on various
tasks. However, how to introduce cascade to instance segmentation remains an open …