no code implementations • 9 Mar 2024 • Yuhao Bian, Shengjing Tian, Xiuping Liu
The widespread deployment of Deep Neural Networks (DNNs) for 3D point cloud processing starkly contrasts with their susceptibility to security breaches, notably backdoor attacks.
no code implementations • 24 Jan 2024 • Shengjing Tian, Yinan Han, Xiuping Liu, Xiantong Zhao
To this end, we propose a Siamese network-based method for small object tracking in the LiDAR point cloud, which is composed of the target-awareness prototype mining (TAPM) module and the regional grid subdivision (RGS) module.
no code implementations • 16 Oct 2022 • Xiantong Zhao, Yinan Han, Shengjing Tian, Jian Liu, Xiuping Liu
Although recent Siamese network-based trackers have achieved impressive perceptual accuracy for single object tracking in LiDAR point clouds, they advance with some heavy correlation operations on relation modeling and overlook the inherent merit of arbitrariness compared to multiple object tracking.
no code implementations • 28 Feb 2022 • Shengjing Tian, Jun Liu, Xiuping Liu
In this work, we investigate a more challenging task in the LiDAR point clouds, class-agnostic tracking, where a general model is supposed to be learned for any specified targets of both observed and unseen categories.