1 code implementation • 13 Aug 2023 • Jie Liao, Jintang Li, Liang Chen, Bingzhe Wu, Yatao Bian, Zibin Zheng
In the pursuit of promoting the expressiveness of GNNs for tail nodes, we explore how the deficiency of structural information deteriorates the performance of tail nodes and propose a general Structural Augmentation based taIL nOde Representation learning framework, dubbed as SAILOR, which can jointly learn to augment the graph structure and extract more informative representations for tail nodes.
1 code implementation • 20 Apr 2022 • Jintang Li, Jie Liao, Ruofan Wu, Liang Chen, Zibin Zheng, Jiawang Dan, Changhua Meng, Weiqiang Wang
To mitigate such a threat, considerable research efforts have been devoted to increasing the robustness of GCNs against adversarial attacks.
no code implementations • 28 Jun 2020 • Zhuangzhuang Ding, Yihan Hu, Runzhou Ge, Li Huang, Sijia Chen, Yu Wang, Jie Liao
We proposed a one-stage, anchor-free and NMS-free 3D point cloud object detector AFDet, using object key-points to encode the 3D attributes, and to learn an end-to-end point cloud object detection without the need of hand-engineering or learning the anchors.
no code implementations • 28 Jun 2020 • Sijia Chen, Yu Wang, Li Huang, Runzhou Ge, Yihan Hu, Zhuangzhuang Ding, Jie Liao
A practical autonomous driving system urges the need to reliably and accurately detect vehicles and persons.
no code implementations • 28 Jun 2020 • Yu Wang, Sijia Chen, Li Huang, Runzhou Ge, Yihan Hu, Zhuangzhuang Ding, Jie Liao
This technical report presents the online and real-time 2D and 3D multi-object tracking (MOT) algorithms that reached the 1st places on both Waymo Open Dataset 2D tracking and 3D tracking challenges.
no code implementations • CVPR 2018 • Yanping Fu, Qingan Yan, Long Yang, Jie Liao, Chunxia Xiao
Acquiring realistic texture details for 3D models is important in 3D reconstruction.