Equivariant Local Reference Frames for Unsupervised Non-rigid Point Cloud Shape Correspondence

no code implementations • 1 Apr 2024 • Ling Wang, Runfa Chen, Yikai Wang, Fuchun Sun, Xinzhou Wang, Sun Kai, Guangyuan Fu, Jianwei Zhang, Wenbing Huang

Based on the assumption of local rigidity, one solution for reducing complexity is to decompose the overall shape into independent local regions using Local Reference Frames (LRFs) that are invariant to SE(3) transformations.

AutoLoss-GMS: Searching Generalized Margin-Based Softmax Loss Function for Person Re-Identification

no code implementations • CVPR 2022 • Hongyang Gu, Jianmin Li, Guangyuan Fu, Chifong Wong, Xinghao Chen, Jun Zhu

In this paper, we propose a novel method, AutoLoss-GMS, to search the better loss function in the space of generalized margin-based softmax loss function for person re-identification automatically.

Person Re-Identification