{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T01:53:57Z","timestamp":1773194037570,"version":"3.50.1"},"reference-count":69,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T00:00:00Z","timestamp":1714435200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T00:00:00Z","timestamp":1714435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LGF21F20012"],"award-info":[{"award-number":["LGF21F20012"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LGF21F20012"],"award-info":[{"award-number":["LGF21F20012"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LGF21F20012"],"award-info":[{"award-number":["LGF21F20012"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LGF21F20012"],"award-info":[{"award-number":["LGF21F20012"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s00530-024-01335-7","type":"journal-article","created":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T01:01:38Z","timestamp":1714438898000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["PointCMC: cross-modal multi-scale correspondences learning for point cloud understanding"],"prefix":"10.1007","volume":"30","author":[{"given":"Honggu","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Xiaogang","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Yikai","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Zizhao","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,30]]},"reference":[{"key":"1335_CR1","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: Deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652\u2013660 (2017)"},{"key":"1335_CR2","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017)"},{"key":"1335_CR3","doi-asserted-by":"crossref","unstructured":"Liu, Y., Fan, B., Xiang, S., Pan, C.: Relation-shape convolutional neural network for point cloud analysis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8895\u20138904 (2019)","DOI":"10.1109\/CVPR.2019.00910"},{"issue":"5","key":"1335_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3326362","volume":"38","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph cnn for learning on point clouds. Acm Trans. Gr. (tog) 38(5), 1\u201312 (2019)","journal-title":"Acm Trans. Gr. (tog)"},{"key":"1335_CR5","unstructured":"Li, Y., Bu, R., Sun, M., Wu, W., Di, X., Chen, B.: Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018)"},{"key":"1335_CR6","doi-asserted-by":"crossref","unstructured":"Wu, W., Qi, Z., Fuxin, L.: Pointconv: Deep convolutional networks on 3d point clouds. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9621\u20139630 (2019)","DOI":"10.1109\/CVPR.2019.00985"},{"key":"1335_CR7","doi-asserted-by":"crossref","unstructured":"Xu, Y., Fan, T., Xu, M., Zeng, L., Qiao, Y.: Spidercnn: Deep learning on point sets with parameterized convolutional filters. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 87\u2013102 (2018)","DOI":"10.1007\/978-3-030-01237-3_6"},{"key":"1335_CR8","unstructured":"Achlioptas, P., Diamanti, O., Mitliagkas, I., Guibas, L.: Learning representations and generative models for 3d point clouds. In: International Conference on Machine Learning, pp. 40\u201349 (2018). PMLR"},{"key":"1335_CR9","doi-asserted-by":"crossref","unstructured":"Li, J., Chen, B.M., Lee, G.H.: So-net: Self-organizing network for point cloud analysis. Proceedings of the IEEE conference on computer vision and pattern recognition (2018)","DOI":"10.1109\/CVPR.2018.00979"},{"key":"1335_CR10","doi-asserted-by":"crossref","unstructured":"Xie, S., Gu, J., Guo, D., Qi, C.R., Guibas, L., Litany, O.: Pointcontrast: Unsupervised pre-training for 3d point cloud understanding. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part III 16, pp. 574\u2013591 (2020). Springer","DOI":"10.1007\/978-3-030-58580-8_34"},{"key":"1335_CR11","doi-asserted-by":"crossref","unstructured":"Wang, P.-S., Yang, Y.-Q., Zou, Q.-F., Wu, Z., Liu, Y., Tong, X.: Unsupervised 3d learning for shape analysis via multiresolution instance discrimination. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 2773\u20132781 (2021)","DOI":"10.1609\/aaai.v35i4.16382"},{"key":"1335_CR12","doi-asserted-by":"crossref","unstructured":"Zhang, L., Zhu, Z.: Unsupervised feature learning for point cloud understanding by contrasting and clustering using graph convolutional neural networks. international conference on 3d vision (2019)","DOI":"10.1109\/3DV.2019.00051"},{"key":"1335_CR13","doi-asserted-by":"crossref","unstructured":"Afham, M., Dissanayake, I., Dissanayake, D., Dharmasiri, A., Thilakarathna, K., Rodrigo, R.: Crosspoint: Self-supervised cross-modal contrastive learning for 3d point cloud understanding. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9902\u20139912 (2022)","DOI":"10.1109\/CVPR52688.2022.00967"},{"key":"1335_CR14","doi-asserted-by":"crossref","unstructured":"Jing, L., Zhang, L., Tian, Y.: Self-supervised feature learning by cross-modality and cross-view correspondences. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2021)","DOI":"10.1109\/CVPRW53098.2021.00174"},{"key":"1335_CR15","unstructured":"Liu, Y.-C., Huang, Y.-K., Chiang, H.-Y., Su, H.-T., Liu, Z.-Y., Chen, C.-T., Tseng, C.-Y., Hsu, W.H.: Learning from 2d: Contrastive pixel-to-point knowledge transfer for 3d pretraining. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2021)"},{"key":"1335_CR16","unstructured":"Liu, Y., Yi, L., Zhang, S., Fan, Q., Funkhouser, T., Dong, H.: P4contrast: Contrastive learning with pairs of point-pixel pairs for rgb-d scene understanding. arXiv: Computer Vision and Pattern Recognition (2020)"},{"key":"1335_CR17","doi-asserted-by":"crossref","unstructured":"Wang, B., Chen, C., Cui, Z., Qin, J., Lu, C.X., Yu, Z., Zhao, P., Dong, Z., Zhu, F., Trigoni, N., et al.: P2-net: Joint description and detection of local features for pixel and point matching. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 16004\u201316013 (2021)","DOI":"10.1109\/ICCV48922.2021.01570"},{"key":"1335_CR18","doi-asserted-by":"crossref","unstructured":"Rao, Y., Lu, J., Zhou, J.: Global-local bidirectional reasoning for unsupervised representation learning of 3d point clouds. Proceedings of the IEEE conference on computer vision and pattern recognition (2020)","DOI":"10.1109\/CVPR42600.2020.00542"},{"key":"1335_CR19","doi-asserted-by":"crossref","unstructured":"Jing, L., Zhang, L., Tian, Y.: Self-supervised feature learning by cross-modality and cross-view correspondences. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1581\u20131591 (2021)","DOI":"10.1109\/CVPRW53098.2021.00174"},{"key":"1335_CR20","doi-asserted-by":"crossref","unstructured":"Qi, C.R., Su, H., Nie\u00dfner, M., Dai, A., Yan, M., Guibas, L.J.: Volumetric and multi-view cnns for object classification on 3d data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5648\u20135656 (2016)","DOI":"10.1109\/CVPR.2016.609"},{"key":"1335_CR21","doi-asserted-by":"crossref","unstructured":"Le, T., Duan, Y.: Pointgrid: A deep network for 3d shape understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9204\u20139214 (2018)","DOI":"10.1109\/CVPR.2018.00959"},{"key":"1335_CR22","doi-asserted-by":"crossref","unstructured":"Thomas, H., Qi, C.R., Deschaud, J.-E., Marcotegui, B., Goulette, F., Guibas, L.J.: Kpconv: Flexible and deformable convolution for point clouds. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6411\u20136420 (2019)","DOI":"10.1109\/ICCV.2019.00651"},{"key":"1335_CR23","doi-asserted-by":"crossref","unstructured":"Doersch, C., Gupta, A., Efros, A.A.: Unsupervised visual representation learning by context prediction. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1422\u20131430 (2015)","DOI":"10.1109\/ICCV.2015.167"},{"key":"1335_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A.: Split-brain autoencoders: Unsupervised learning by cross-channel prediction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1058\u20131067 (2017)","DOI":"10.1109\/CVPR.2017.76"},{"key":"1335_CR25","unstructured":"Bachman, P., Hjelm, R.D., Buchwalter, W.: Learning representations by maximizing mutual information across views. Advances in neural information processing systems 32 (2019)"},{"key":"1335_CR26","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.E.: A simple framework for contrastive learning of visual representations. international conference on machine learning (2020)"},{"key":"1335_CR27","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"1335_CR28","doi-asserted-by":"crossref","unstructured":"Sanghi, A.: Info3d: Representation learning on 3d objects using mutual information maximization and contrastive learning. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXIX 16, pp. 626\u2013642 (2020). Springer","DOI":"10.1007\/978-3-030-58526-6_37"},{"key":"1335_CR29","first-page":"21271","volume":"33","author":"J-B Grill","year":"2020","unstructured":"Grill, J.-B., Strub, F., Altch\u00e9, F., Tallec, C., Richemond, P., Buchatskaya, E., Doersch, C., Avila Pires, B., Guo, Z., Gheshlaghi Azar, M., et al.: Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems 33, 21271\u201321284 (2020)","journal-title":"Advances in neural information processing systems"},{"key":"1335_CR30","doi-asserted-by":"crossref","unstructured":"Kramer, M.A.: Nonlinear principal component analysis using autoassociative neural networks. AIChE journal, 233\u2013243 (1991)","DOI":"10.1002\/aic.690370209"},{"key":"1335_CR31","doi-asserted-by":"crossref","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Communications of the ACM, 139\u2013144 (2020)","DOI":"10.1145\/3422622"},{"key":"1335_CR32","doi-asserted-by":"crossref","unstructured":"Yang, Y., Feng, C., Shen, Y., Tian, D.: Foldingnet: Point cloud auto-encoder via deep grid deformation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 206\u2013215 (2018)","DOI":"10.1109\/CVPR.2018.00029"},{"key":"1335_CR33","doi-asserted-by":"crossref","unstructured":"Fan, H., Su, H., Guibas, L.J.: A point set generation network for 3d object reconstruction from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 605\u2013613 (2017)","DOI":"10.1109\/CVPR.2017.264"},{"key":"1335_CR34","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Birdal, T., Deng, H., Tombari, F.: 3d point capsule networks. Proceedings of the IEEE conference on computer vision and pattern recognition (2018)","DOI":"10.1109\/CVPR.2019.00110"},{"key":"1335_CR35","doi-asserted-by":"crossref","unstructured":"Han, Z., Wang, X., Liu, Y.-S., Zwicker, M.: Multi-angle point cloud-vae: Unsupervised feature learning for 3d point clouds from multiple angles by joint self-reconstruction and half-to-half prediction. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 10441\u201310450 (2019). IEEE","DOI":"10.1109\/ICCV.2019.01054"},{"key":"1335_CR36","doi-asserted-by":"crossref","unstructured":"Li, R., Li, X., Fu, C.-W., Cohen-Or, D., Heng, P.-A.: Pu-gan: A point cloud upsampling adversarial network. international conference on computer vision (2019)","DOI":"10.1109\/ICCV.2019.00730"},{"key":"1335_CR37","doi-asserted-by":"crossref","unstructured":"Yu, L., Li, X., Fu, C.-W., Cohen-Or, D., Heng, P.-A.: Pu-net: Point cloud upsampling network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2790\u20132799 (2018)","DOI":"10.1109\/CVPR.2018.00295"},{"key":"1335_CR38","doi-asserted-by":"crossref","unstructured":"Li, R., Li, X., Heng, P.-A., Fu, C.-W.: Point cloud upsampling via disentangled refinement. Proceedings of the IEEE conference on computer vision and pattern recognition (2021)","DOI":"10.1109\/CVPR46437.2021.00041"},{"key":"1335_CR39","doi-asserted-by":"crossref","unstructured":"Wang, H., Liu, Q., Yue, X., Lasenby, J., Kusner, M.J.: Unsupervised point cloud pre-training via occlusion completion. international conference on computer vision (2021)","DOI":"10.1109\/ICCV48922.2021.00964"},{"key":"1335_CR40","doi-asserted-by":"crossref","unstructured":"Huang, Z., Yu, Y., Xu, J., Ni, F., Le, X.: Pf-net: Point fractal network for 3d point cloud completion. Proceedings of the IEEE conference on computer vision and pattern recognition (2020)","DOI":"10.1109\/CVPR42600.2020.00768"},{"key":"1335_CR41","doi-asserted-by":"crossref","unstructured":"Sharma, A., Grau, O., Fritz, M.: Vconv-dae: Deep volumetric shape learning without object labels. Proceedings of the IEEE conference on computer vision and pattern recognition (2016)","DOI":"10.1007\/978-3-319-49409-8_20"},{"key":"1335_CR42","doi-asserted-by":"crossref","unstructured":"Xie, J., Zheng, Z., Gao, R., Wang, W., Zhu, S.-C., Wu, Y.N.: Learning descriptor networks for 3d shape synthesis and analysis. Proceedings of the IEEE conference on computer vision and pattern recognition (2018)","DOI":"10.1109\/CVPR.2018.00900"},{"key":"1335_CR43","unstructured":"Valsesia, D., Fracastoro, G., Magli, E.: Learning localized generative models for 3d point clouds via graph convolution. international conference on learning representations (2018)"},{"key":"1335_CR44","unstructured":"Li, C.-L., Zaheer, M., Zhang, Y., Poczos, B., Salakhutdinov, R.: Point cloud gan. arXiv preprint arXiv:1810.05795 (2018)"},{"key":"1335_CR45","doi-asserted-by":"crossref","unstructured":"Du, B., Gao, X., Hu, W., Li, X.: Self-contrastive learning with hard negative sampling for self-supervised point cloud learning. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 3133\u20133142 (2021)","DOI":"10.1145\/3474085.3475458"},{"key":"1335_CR46","doi-asserted-by":"crossref","unstructured":"Huang, S., Xie, Y., Zhu, S.-C., Zhu, Y.: Spatio-temporal self-supervised representation learning for 3d point clouds. international conference on computer vision (2021)","DOI":"10.1109\/ICCV48922.2021.00647"},{"key":"1335_CR47","unstructured":"Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Learning to prompt for vision-language models. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2021)"},{"key":"1335_CR48","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I.: Learning transferable visual models from natural language supervision. international conference on machine learning (2021)"},{"key":"1335_CR49","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.cag.2017.07.013","volume":"70","author":"Z Wu","year":"2018","unstructured":"Wu, Z., Zhang, Y., Zeng, M., Qin, F., Wang, Y.: Joint analysis of shapes and images via deep domain adaptation. Computers & Graphics 70, 140\u2013147 (2018)","journal-title":"Computers & Graphics"},{"key":"1335_CR50","unstructured":"Yan, X., Zhan, H., Zheng, C., Gao, J., Zhang, R., Cui, S., Li, Z.: Let images give you more: Point cloud cross-modal training for shape analysis. arXiv preprint arXiv:2210.04208 (2022)"},{"key":"1335_CR51","unstructured":"Xiao, A., Huang, J., Guan, D., Lu, S.: Unsupervised representation learning for point clouds: A survey. arXiv preprint arXiv:2202.13589 (2022)"},{"key":"1335_CR52","doi-asserted-by":"crossref","unstructured":"Xu, C., Yang, S., Zhai, B., Wu, B., Yue, X., Zhan, W., Vajda, P., Keutzer, K., Tomizuka, M.: Image2point: 3d point-cloud understanding with pretrained 2d convnets. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2021)","DOI":"10.1007\/978-3-031-19836-6_36"},{"key":"1335_CR53","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Girdhar, R., Joulin, A., Misra, I.: Self-supervised pretraining of 3d features on any point-cloud. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10252\u201310263 (2021)","DOI":"10.1109\/ICCV48922.2021.01009"},{"key":"1335_CR54","unstructured":"Chang, A.X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., et al.: Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012 (2015)"},{"key":"1335_CR55","unstructured":"Xu, Q., Wang, W., Ceylan, D., Mech, R., Neumann, U.: Disn: Deep implicit surface network for high-quality single-view 3d reconstruction. neural information processing systems (2019)"},{"key":"1335_CR56","unstructured":"Wu, J., Zhang, C., Xue, T., Freeman, W.T., Tenenbaum, J.B.: Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling. neural information processing systems (2016)"},{"key":"1335_CR57","doi-asserted-by":"crossref","unstructured":"Gadelha, M., Wang, R., Maji, S.: Multiresolution tree networks for 3d point cloud processing. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 103\u2013118 (2018)","DOI":"10.1007\/978-3-030-01234-2_7"},{"key":"1335_CR58","doi-asserted-by":"crossref","unstructured":"Han, Z., Shang, M., Liu, Y.-S., Zwicker, M.: View inter-prediction gan: Unsupervised representation learning for 3d shapes by learning global shape memories to support local view predictions. national conference on artificial intelligence (2019)","DOI":"10.1609\/aaai.v33i01.33018376"},{"key":"1335_CR59","doi-asserted-by":"crossref","unstructured":"Hassani, K., Haley, M.: Unsupervised multi-task feature learning on point clouds. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8160\u20138171 (2019)","DOI":"10.1109\/ICCV.2019.00825"},{"key":"1335_CR60","unstructured":"Sauder, J., Sievers, B.: Self-supervised deep learning on point clouds by reconstructing space. neural information processing systems (2019)"},{"key":"1335_CR61","doi-asserted-by":"crossref","unstructured":"Poursaeed, O., Jiang, T., Qiao, H., Xu, N., Kim, V.G.: Self-supervised learning of point clouds via orientation estimation. international conference on 3d vision (2020)","DOI":"10.1109\/3DV50981.2020.00112"},{"key":"1335_CR62","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. neural information processing systems (2019)"},{"key":"1335_CR63","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"1335_CR64","unstructured":"Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)"},{"key":"1335_CR65","unstructured":"Sharma, C., Kaul, M.: Self-supervised few-shot learning on point clouds. neural information processing systems (2020)"},{"key":"1335_CR66","doi-asserted-by":"crossref","unstructured":"Yi, L., Kim, V.G., Ceylan, D., Shen, I.-C., Yan, M., Su, H., Lu, C., Huang, Q., Sheffer, A., Guibas, L.J.: A scalable active framework for region annotation in 3d shape collections. international conference on computer graphics and interactive techniques (2016)","DOI":"10.1145\/2980179.2980238"},{"key":"1335_CR67","unstructured":"Armeni, I., Sax, A., Zamir, A.R., Savarese, S.: Joint 2D-3D-Semantic Data for Indoor Scene Understanding. ArXiv e-prints (2017) arXiv:1702.01105 [cs.CV]"},{"key":"1335_CR68","unstructured":"van\u00a0der Maaten, L., Hinton, G.E.: Visualizing data using t-sne. Journal of Machine Learning Research (2008)"},{"key":"1335_CR69","unstructured":"Liu, F., Lin, G., Foo, C.-S.: Point discriminative learning for unsupervised representation learning on 3d point clouds. Proceedings of the IEEE\/CVF International Conference on Computer Vision (2021)"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01335-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-024-01335-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01335-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T13:15:45Z","timestamp":1720185345000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-024-01335-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,30]]},"references-count":69,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["1335"],"URL":"https:\/\/doi.org\/10.1007\/s00530-024-01335-7","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-2772055\/v1","asserted-by":"object"}]},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,30]]},"assertion":[{"value":"3 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 April 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"138"}}