TripletMix: Triplet Data Augmentation for 3D Understanding

no code implementations • 28 May 2024 • Jiaze Wang, Yi Wang, Ziyu Guo, Renrui Zhang, Donghao Zhou, Guangyong Chen, Anfeng Liu, Pheng-Ann Heng

Data augmentation has proven to be a vital tool for enhancing the generalization capabilities of deep learning models, especially in the context of 3D vision where traditional datasets are often limited.

PointPatchMix: Point Cloud Mixing with Patch Scoring

no code implementations • 12 Mar 2023 • Yi Wang, Jiaze Wang, Jinpeng Li, Zixu Zhao, Guangyong Chen, Anfeng Liu, Pheng-Ann Heng

With Point-MAE as our baseline, our model surpasses previous methods by a significant margin, achieving 86. 3% accuracy on ScanObjectNN and 94. 1% accuracy on ModelNet40.

Data Augmentation

A Semi-supervised Sensing Rate Learning based CMAB Scheme to Combat COVID-19 by Trustful Data Collection in the Crowd

no code implementations • 17 Jan 2023 • Jianheng Tang, Kejia Fan, Wenxuan Xie, Luomin Zeng, Feijiang Han, Guosheng Huang, Tian Wang, Anfeng Liu, Shaobo Zhang

In this paper, an incentive mechanism named Semi-supervision based Combinatorial Multi-Armed Bandit reverse Auction (SCMABA) is proposed to solve the recruitment problem of multiple unknown and strategic workers in MCS.