Hard-Soft Pseudo Labels Guided Semi-Supervised Learning for Point Cloud Classification
文献类型：期刊论文
| 作者 | He, Yuan1,2 ; Hu, Guyue3; Yu, Shan1,4,5
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| 刊名 | IEEE SIGNAL PROCESSING LETTERS
 |
| 出版日期 | 2024 |
| 卷号 | 31页码:1059-1063 |
| 关键词 | Point cloud compression Training Three-dimensional displays Self-supervised learning Semisupervised learning Task analysis Unsupervised learning Point cloud 3D vision semi-supervised learning contrastive learning pseudo label |
| ISSN号 | 1070-9908 |
| DOI | 10.1109/LSP.2024.3386115 |
| 通讯作者 | Hu, Guyue(guyue.hu@ahu.edu.cn) |
| 英文摘要 | Point clouds are widely applied in 3D visual sensing and perception. However, manually annotating point clouds is much more tedious and time-consuming than that for 2D images. Fortunately, semi-supervised learning can leverage massive unlabeled data to alleviate this issue, which is becoming a promising technique nowadays. In this letter, we propose a novel semi-supervised learning (SSL) framework for point cloud classification, named HPSSL. Its unsupervised learning branch performs both the representation embedding and pseudo-classification tasks. Specifically, both hard and soft pseudo labels of unlabeled samples are generated from a shared classifier to guide the class-aware contrastive learning in our SSL framework. Besides, a prediction consistency strategy is proposed to enhance the discrimination of feature representation and the exactness of pseudo labels. Furthermore, we force the supervised learning branch to interact with the unsupervised learning branch via distribution alignment, thus achieving representation consistency. Extensive experiments on three 3D shape recognition benchmarks demonstrate the effectiveness of the proposed approach. |
| WOS关键词 | NETWORK |
| 资助项目 | STI 2030-Major Project |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001204993300002 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | STI 2030-Major Project |
| 源URL | [http://ir.ia.ac.cn/handle/173211/58688]  |
| 专题 | 脑机接口与融合智能 |
| 通讯作者 | Hu, Guyue |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Lab Brain Atlas & Brain Inspired Intelligence, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Anhui Univ, Sch Artificial Intelligence, Informat Mat & Intelligent Sensing Lab Anhui Prov, Anhui Prov Key Lab Multimodal Cognit Computat, Hefei 230601, Peoples R China 4.Chinese Acad Sci, Key Lab Brain Cognit & Brain Inspired Intelligence, Beijing 100049, Peoples R China 5.Univ Chinese Acad Sci, Sch Future Technol, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | He, Yuan,Hu, Guyue,Yu, Shan. Hard-Soft Pseudo Labels Guided Semi-Supervised Learning for Point Cloud Classification[J]. IEEE SIGNAL PROCESSING LETTERS,2024,31:1059-1063. |
| APA | He, Yuan,Hu, Guyue,&Yu, Shan.(2024).Hard-Soft Pseudo Labels Guided Semi-Supervised Learning for Point Cloud Classification.IEEE SIGNAL PROCESSING LETTERS,31,1059-1063. |
| MLA | He, Yuan,et al."Hard-Soft Pseudo Labels Guided Semi-Supervised Learning for Point Cloud Classification".IEEE SIGNAL PROCESSING LETTERS 31(2024):1059-1063. |
入库方式： OAI收割
来源：自动化研究所
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