面向多姿态点云目标的在线类增量学习

张润江; 郭杰龙; 俞辉; 兰海; 王希豪; 魏宪

doi:10.37188/CJLCD.2022-0419

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面向多姿态点云目标的在线类增量学习

目标检测与识别 | 更新时间：2023-11-03

- 面向多姿态点云目标的在线类增量学习
- Online class incremental learning for multi-pose point cloud targets
- 液晶与显示 2023年38卷第11期页码：1542-1553
- 作者机构：
  
  1.福州大学电气工程与自动化学院，福建福州 350108
  2.中国科学院福建物质结构研究所，福建福州 350002
  3.中国科学院海西研究院泉州装备制造研究中心，福建泉州 362000
- 作者简介：
  
  [ "张润江（1998—），男，山西运城人，硕士研究生，2020年于福建工程学院获得学士学位，主要从事计算机视觉及持续学习方面的研究。E-mail： 1069707145@qq.com" ]
  [ "魏宪（1986—），男，河南沁阳人，博士，研究员，2017年于慕尼黑工业大学获得博士学位，主要从事机器学习、几何优化方面的研究。E-mail：xian.wei@fjirsm.ac.cn" ]
- 基金信息：
  
  福建省科技计划(2021T3003);泉州市科技计划(2021C065L)
- DOI：10.37188/CJLCD.2022-0419
  中图分类号：
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张润江, 郭杰龙, 俞辉, 等. 面向多姿态点云目标的在线类增量学习[J]. 液晶与显示, 2023,38(11):1542-1553.

ZHANG Run-jiang, GUO Jie-long, YU Hui, et al. Online class incremental learning for multi-pose point cloud targets[J]. Chinese Journal of Liquid Crystals and Displays, 2023,38(11):1542-1553.
张润江, 郭杰龙, 俞辉, 等. 面向多姿态点云目标的在线类增量学习[J]. 液晶与显示, 2023,38(11):1542-1553. DOI： 10.37188/CJLCD.2022-0419.

ZHANG Run-jiang, GUO Jie-long, YU Hui, et al. Online class incremental learning for multi-pose point cloud targets[J]. Chinese Journal of Liquid Crystals and Displays, 2023,38(11):1542-1553. DOI： 10.37188/CJLCD.2022-0419.

摘要

针对目前增量学习中所面向目标都是固定姿态这一现象，本文考虑了更严格的设定，即面向多姿态目标的在线类增量学习，并提出了无视姿态重放方法来缓解在线类增量学习中面对多姿态目标时的灾难性遗忘。首先，将2D/3D目标进行点云化处理，以方便提取目标的有效几何信息；其次，基于, $S E (d) (d = 2,3)$ ,https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=49588130&type=,https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=49588142&type=,22.77533340,3.04800010,群对网络进行平移旋转等变性改进，使网络能够提取更丰富的几何信息，从而降低模型在每个任务中受目标姿态的影响；最后，根据损失变化采样特定样本用于重放来缓解灾难性遗忘。实验结果表明，在面对固定姿态目标MNIST、CIFAR-10时，本文方法的最终平均精度分别达到了88%和42.6%，与对比方法结果相近，但最终平均遗忘率明显优于对比方法，分别降低了约3%和15%。在面对多姿态目标RotMNIST、trCIFAR-10时，本文方法依旧能很好地保持在固定姿态目标中的表现，基本不受目标姿态的影响。此外，在3D数据集ModelNet40中的表现也依旧稳定。本文所提方法在在线类增量学习中能够不受目标姿态的影响，同时能缓解灾难性遗忘，具有很好的稳定性和可塑性。

Abstract

In response to current phenomenon that all targets in incremental learning are fixed pose， this paper considers a more rigorous setting， ,i.‍e., online class incremental learning for multi-pose targets， which innovatively proposes an ignoring pose replay method to alleviate the catastrophic forgetting in facing multi-pose targets in online class incremental learning. Firstly， 2D/3D targets are point-clouded to facilitate the extraction of useful geometric information. Secondly， the network modifies for equivariance based on the , $S E (d) (d = 2,3)$ ,https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=49588850&type=,https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=49588839&type=,26.58533096,3.55599999, group to enable the network to extract richer geometric information， thus reducing the impact of target poses on the model in each task. Finally， specific samples are sampled for replay to mitigate catastrophic forgetting based on loss variation. Experimental results show that when facing fixed posture targets MNIST and CIFAR-10， final average accuracy reaches to 88% and 42.6% respectively， which is comparable to the comparison method， and final average forgetting is significantly better than the comparison method， with a reduction of about 3% and 15% respectively. In the case of the multi-pose target RotMNIST and trCIFAR-10， the proposed method continues to perform well in fixed-pose targets， largely independent of target pose. In addition， the performance in 3D datasets ModelNet40 and trModelNet40 remains stable. The method proposed is able to be independent of the target pose in online class incremental learning， while achieving catastrophic forgetting mitigation， with excellent stability and plasticity.

关键词

在线类增量学习灾难性遗忘无视姿态重放等变性点云分类

Keywords

online class-incremental learningcatastrophic forgettingignoring pose replayequivariancepoint cloud classification

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