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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2021,8,31]]},"abstract":"<jats:p>Three-dimensional (3D) shape recognition is a popular topic and has potential application value in the field of computer vision. With the recent proliferation of deep learning, various deep learning models have achieved state-of-the-art performance. Among them, multiview-based 3D shape representation has received increased attention in recent years, and related approaches have shown significant improvement in 3D shape recognition. However, these methods focus on feature learning based on the design of the network and ignore the correlation among views. In this article, we propose a novel progressive feature guide learning network (PGNet) that focuses on the correlation among multiple views and integrates multiple modalities for 3D shape recognition. In particular, we propose two information fusion schemes from visual and feature aspects. The visual fusion scheme focuses on the view level and employs the soft-attention model to define the weights of views for visual information fusion. The feature fusion scheme focuses on the feature dimension information and employs the quantified feature as the mask to further optimize the feature. These two schemes jointly construct a PGNet for 3D shape representation. The classic ModelNet40 and ShapeNetCore55 datasets are applied to demonstrate the performance of our approach. The corresponding experiment also demonstrates the superiority of our approach.<\/jats:p>","DOI":"10.1145\/3443708","type":"journal-article","created":{"date-parts":[[2021,7,22]],"date-time":"2021-07-22T14:44:29Z","timestamp":1626965069000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["PGNet: Progressive Feature Guide Learning Network for Three-dimensional Shape Recognition"],"prefix":"10.1145","volume":"17","author":[{"given":"Jie","family":"Nie","sequence":"first","affiliation":[{"name":"The College of Information Science and Engineering, Ocean University of China, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhi-Qiang","family":"Wei","sequence":"additional","affiliation":[{"name":"The College of Information Science and Engineering, Ocean University of China, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weizhi","family":"Nie","sequence":"additional","affiliation":[{"name":"The School of Electrical and Information Engineering,Tianjin University, TianJin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"An-An","family":"Liu","sequence":"additional","affiliation":[{"name":"The School of Electrical and Information Engineering, Tianjin University, TianJin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,7,22]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.114"},{"key":"e_1_2_1_2_1","unstructured":"Chu Wang Marcello Pelillo and Kaleem Siddiqi. 2019. Dominant set clustering and pooling for multi-view 3d object recognition. arXiv:1906.01592. Retrieved from https:\/\/arxiv.org\/abs\/1906.01592.  Chu Wang Marcello Pelillo and Kaleem Siddiqi. 2019. Dominant set clustering and pooling for multi-view 3d object recognition. arXiv:1906.01592. Retrieved from https:\/\/arxiv.org\/abs\/1906.01592."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2862625"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33018513"},{"volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 652\u2013660","author":"Qi Charles R.","key":"e_1_2_1_5_1","unstructured":"Charles R. Qi , Hao Su , Kaichun Mo , and Leonidas J. Guibas . 2017. Pointnet: Deep learning on point sets for 3d classification and segmentation . In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 652\u2013660 . Charles R. Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas. 2017. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 652\u2013660."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2018.2852310"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2019.2942688"},{"volume-title":"Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS\u201912)","author":"Socher Richard","key":"e_1_2_1_8_1","unstructured":"Richard Socher , Brody Huval , Bharath Putta Bath , Christopher D. Manning , and Andrew Y. Ng . 2012. Convolutional-recursive deep learning for 3d object classification . In Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS\u201912) . 665\u2013673. Richard Socher, Brody Huval, Bharath Putta Bath, Christopher D. Manning, and Andrew Y. Ng. 2012. Convolutional-recursive deep learning for 3d object classification. 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