activity
20182021
most citedACM-Net: Action Context Modeling Network for Weakly-Supervised Temporal Action Localization

44 citations · 61 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV202144 cited

ACM-Net: Action Context Modeling Network for Weakly-Supervised Temporal Action Localization

Sanqing Qu, Guang Chen, Zhijun Li +3

Weakly-supervised temporal action localization aims to localize action instances temporal boundary and identify the corresponding action category with only video-level labels. Trad…

cs.CV20201 cited

PointINet: Point Cloud Frame Interpolation Network

Fan Lu, Guang Chen, Sanqing Qu +3

LiDAR point cloud streams are usually sparse in time dimension, which is limited by hardware performance. Generally, the frame rates of mechanical LiDAR sensors are 10 to 20 Hz, wh…

cs.CV20203 cited

MoNet: Motion-based Point Cloud Prediction Network

Fan Lu, Guang Chen, Yinlong Liu +3

Predicting the future can significantly improve the safety of intelligent vehicles, which is a key component in autonomous driving. 3D point clouds accurately model 3D information…

cs.CV202012 cited

LAP-Net: Adaptive Features Sampling via Learning Action Progression for Online Action Detection

Sanqing Qu, Guang Chen, Dan Xu +3

Online action detection is a task with the aim of identifying ongoing actions from streaming videos without any side information or access to future frames. Recent methods proposed…

cs.CV2020

RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor

Fan Lu, Guang Chen, Yinlong Liu +2

Keypoint detector and descriptor are two main components of point cloud registration. Previous learning-based keypoint detectors rely on saliency estimation for each point or farth…

cs.RO20181 cited

An Efficient L-Shape Fitting Method for Vehicle Pose Detection with 2D LiDAR

Sanqing Qu, Guang Chen, Canbo Ye +4

Detecting vehicles with strong robustness and high efficiency has become one of the key capabilities of fully autonomous driving cars. This topic has already been widely studied by…