Semi‐Supervised 3D Shape Recognition via Multimodal Deep Co‐training
3D shape recognition has been actively investigated in the field of computer graphics. With the rapid development of deep learning, various deep models have been introduced and achieved remarkable results. Most 3D shape recognition methods are supervised and learn only from the large amount of label...
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| Veröffentlicht in: | Computer graphics forum Jg. 39; H. 7; S. 279 - 289 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Blackwell Publishing Ltd
01.10.2020
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| ISSN: | 0167-7055, 1467-8659 |
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| Abstract | 3D shape recognition has been actively investigated in the field of computer graphics. With the rapid development of deep learning, various deep models have been introduced and achieved remarkable results. Most 3D shape recognition methods are supervised and learn only from the large amount of labeled shapes. However, it is expensive and time consuming to obtain such a large training set. In contrast to these methods, this paper studies a semi‐supervised learning framework to train a deep model for 3D shape recognition by using both labeled and unlabeled shapes. Inspired by the co‐training algorithm, our method iterates between model training and pseudo‐label generation phases. In the model training phase, we train two deep networks based on the point cloud and multi‐view representation simultaneously. In the pseudo‐label generation phase, we generate the pseudo‐labels of the unlabeled shapes using the joint prediction of two networks, which augments the labeled set for the next iteration. To extract more reliable consensus information from multiple representations, we propose an uncertainty‐aware consistency loss function to combine the two networks into a multimodal network. This not only encourages the two networks to give similar predictions on the unlabeled set, but also eliminates the negative influence of the large performance gap between the two networks. Experiments on the benchmark ModelNet40 demonstrate that, with only 10% labeled training data, our approach achieves competitive performance to the results reported by supervised methods. |
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| AbstractList | 3D shape recognition has been actively investigated in the field of computer graphics. With the rapid development of deep learning, various deep models have been introduced and achieved remarkable results. Most 3D shape recognition methods are supervised and learn only from the large amount of labeled shapes. However, it is expensive and time consuming to obtain such a large training set. In contrast to these methods, this paper studies a semi‐supervised learning framework to train a deep model for 3D shape recognition by using both labeled and unlabeled shapes. Inspired by the co‐training algorithm, our method iterates between model training and pseudo‐label generation phases. In the model training phase, we train two deep networks based on the point cloud and multi‐view representation simultaneously. In the pseudo‐label generation phase, we generate the pseudo‐labels of the unlabeled shapes using the joint prediction of two networks, which augments the labeled set for the next iteration. To extract more reliable consensus information from multiple representations, we propose an uncertainty‐aware consistency loss function to combine the two networks into a multimodal network. This not only encourages the two networks to give similar predictions on the unlabeled set, but also eliminates the negative influence of the large performance gap between the two networks. Experiments on the benchmark ModelNet40 demonstrate that, with only 10% labeled training data, our approach achieves competitive performance to the results reported by supervised methods. |
| Author | Song, Mofei Liu, Yu Liu, Xiao Fan |
| Author_xml | – sequence: 1 givenname: Mofei orcidid: 0000-0002-9912-1560 surname: Song fullname: Song, Mofei organization: Nanjing University – sequence: 2 givenname: Yu orcidid: 0000-0002-2207-1569 surname: Liu fullname: Liu, Yu organization: Southeast University – sequence: 3 givenname: Xiao Fan orcidid: 0000-0001-8342-4623 surname: Liu fullname: Liu, Xiao Fan organization: City University of Hong Kong |
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| Cites_doi | 10.1609/aaai.v33i01.33018279 10.1007/978-3-319-49409-8_20 10.1109/CVPR.2017.238 10.1145/3240508.3240702 10.1109/TMM.2017.2758522 10.1109/CGIV.2013.11 10.1109/TVCG.2018.2866793 10.1109/ACCESS.2020.2982196 10.1109/ICCV.2015.114 10.1109/TMM.2014.2351788 10.1109/TPAMI.2018.2858821 10.1109/CVPR.2019.00521 10.1109/WACV45572.2020.9093608 10.1016/j.neucom.2018.01.080 10.1609/aaai.v33i01.33018376 10.1145/2508363.2508364 10.1007/978-3-031-01548-9_7 10.1007/978-3-030-11015-4_49 10.1109/TIP.2016.2563981 10.1145/3326362 10.1109/CVPR42600.2020.01109 10.1109/ICME.2014.6890145 10.1145/279943.279962 10.1109/TIP.2019.2933724 10.1016/j.patcog.2020.107269 10.1609/aaai.v33i01.33019119 10.1145/3343031.3351009 10.1109/CVPR.2018.00029 10.1109/TPAMI.2014.2299812 10.1109/CVPR.2019.00910 10.1109/CVPR.2019.00997 10.1111/cgf.12790 10.1016/j.patcog.2019.107164 10.1145/3272127.3275050 |
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| Title | Semi‐Supervised 3D Shape Recognition via Multimodal Deep Co‐training |
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