1.3k citations · 1.4k across the 7 of their papers we have counts for
7 papers
ImpDet: Exploring Implicit Fields for 3D Object Detection
Xuelin Qian, Li Wang, Yi Zhu +3
Conventional 3D object detection approaches concentrate on bounding boxes representation learning with several parameters, i.e., localization, dimension, and orientation. Despite i…
Progressive Coordinate Transforms for Monocular 3D Object Detection
Li Wang, Li Zhang, Yi Zhu +4
Recognizing and localizing objects in the 3D space is a crucial ability for an AI agent to perceive its surrounding environment. While significant progress has been achieved with e…
Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection
Li Wang, Liang Du, Xiaoqing Ye +5
The objective of this paper is to learn context- and depth-aware feature representation to solve the problem of monocular 3D object detection. We make following contributions: (i)…
CODA: Counting Objects via Scale-aware Adversarial Density Adaption
Li Wang, Yongbo Li, Xiangyang Xue
Recent advances in crowd counting have achieved promising results with increasingly complex convolutional neural network designs. However, due to the unpredictable domain shift, ge…
Crowd Counting with Density Adaption Networks
Li Wang, Weiyuan Shao, Yao Lu +3
Crowd counting is one of the core tasks in various surveillance applications. A practical system involves estimating accurate head counts in dynamic scenarios under different light…
Evolving Boxes for Fast Vehicle Detection
Li Wang, Yao Lu, Hong Wang +3
We perform fast vehicle detection from traffic surveillance cameras. A novel deep learning framework, namely Evolving Boxes, is developed that proposes and refines the object boxes…