794 citations · 871 across the 5 of their papers we have counts for
7 papers
MimicDet: Bridging the Gap Between One-Stage and Two-Stage Object Detection
Xin Lu, Quanquan Li, Buyu Li +1
Modern object detection methods can be divided into one-stage approaches and two-stage ones. One-stage detectors are more efficient owing to straightforward architectures, but the…
Equalization Loss for Long-Tailed Object Recognition
Jingru Tan, Changbao Wang, Buyu Li +4
Object recognition techniques using convolutional neural networks (CNN) have achieved great success. However, state-of-the-art object detection methods still perform poorly on larg…
MMDetection: Open MMLab Detection Toolbox and Benchmark
Kai Chen, Jiaqi Wang, Jiangmiao Pang +22
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. The to…
Grid R-CNN Plus: Faster and Better
Xin Lu, Buyu Li, Yuxin Yue +2
Grid R-CNN is a well-performed objection detection framework. It transforms the traditional box offset regression problem into a grid point estimation problem. With the guidance of…
GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving
Buyu Li, Wanli Ouyang, Lu Sheng +2
We present an efficient 3D object detection framework based on a single RGB image in the scenario of autonomous driving. Our efforts are put on extracting the underlying 3D informa…
Grid R-CNN
Xin Lu, Buyu Li, Yuxin Yue +2
This paper proposes a novel object detection framework named Grid R-CNN, which adopts a grid guided localization mechanism for accurate object detection. Different from the traditi…