{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T03:41:19Z","timestamp":1772941279469,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T00:00:00Z","timestamp":1731715200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T00:00:00Z","timestamp":1731715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["[62276164, 61602296]"],"award-info":[{"award-number":["[62276164, 61602296]"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"\u2019Science and technology innovation action plan\u2019 Natural Science Foundation of Shanghai","award":["[22ZR1427000]"],"award-info":[{"award-number":["[22ZR1427000]"]}]},{"name":"Shanghai Oriental Talent Program-Youth Program"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1007\/s00371-024-03710-x","type":"journal-article","created":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T19:22:00Z","timestamp":1731784920000},"page":"5105-5121","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["DMVMLC-VT: Deep incomplete multi-view multi-label image classification with view translation and pseudo-label enhancement"],"prefix":"10.1007","volume":"41","author":[{"given":"Yanchen","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changming","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,16]]},"reference":[{"key":"3710_CR1","doi-asserted-by":"publisher","unstructured":"Gon\u00e7alves, E.C., Freitas, A.A., Plastino, A.: A survey of genetic algorithms for multi-label classification. In: 2018 IEEE Congress on Evolutionary Computation (CEC), pp. 1\u20138. IEEE, Rio de Janeiro, Brazil (2018). https:\/\/doi.org\/10.1109\/CEC.2018.8477927","DOI":"10.1109\/CEC.2018.8477927"},{"key":"3710_CR2","doi-asserted-by":"publisher","first-page":"2369","DOI":"10.1007\/s10994-020-05915-2","volume":"109","author":"N Otani","year":"2020","unstructured":"Otani, N., Otsubo, Y., Koike, T., Sugiyama, M.: Binary classification with ambiguous training data. Mach. Learn. 109, 2369\u20132388 (2020). https:\/\/doi.org\/10.1007\/s10994-020-05915-2","journal-title":"Mach. Learn."},{"key":"3710_CR3","doi-asserted-by":"publisher","first-page":"42241","DOI":"10.1007\/s11042-021-11451-5","volume":"81","author":"KG Sharma","year":"2022","unstructured":"Sharma, K.G., Singh, Y.: KDV classifier: a novel approach for binary classification. Multimedia Tools Appl. 81, 42241\u201342259 (2022). https:\/\/doi.org\/10.1007\/s11042-021-11451-5","journal-title":"Multimedia Tools Appl."},{"key":"3710_CR4","doi-asserted-by":"publisher","first-page":"8627","DOI":"10.1007\/s00500-023-08048-5","volume":"27","author":"M Ferrandin","year":"2023","unstructured":"Ferrandin, M., Cerri, R.: Multi-label classification via closed frequent labelsets and label taxonomies. Soft. Comput. 27, 8627\u20138660 (2023). https:\/\/doi.org\/10.1007\/s00500-023-08048-5","journal-title":"Soft. Comput."},{"key":"3710_CR5","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1109\/TETCI.2023.3300303","volume":"8","author":"B Yu","year":"2024","unstructured":"Yu, B., Xie, H., Cai, M., Ding, W.: MG-GCN: Multi-granularity graph convolutional neural network for multi-label classification in multi-label information system. IEEE Trans. Emerg. Topics Computat. Intell. 8, 288\u2013299 (2024). https:\/\/doi.org\/10.1109\/TETCI.2023.3300303","journal-title":"IEEE Trans. Emerg. Topics Computat. Intell."},{"key":"3710_CR6","doi-asserted-by":"publisher","first-page":"2199","DOI":"10.1109\/TCSVT.2021.3089480","volume":"32","author":"Z Peng","year":"2022","unstructured":"Peng, Z., Jia, Y., Liu, H., Hou, J., Zhang, Q.: Maximum entropy subspace clustering network. IEEE Trans. Circuits Syst. Video Technol. 32, 2199\u20132210 (2022). https:\/\/doi.org\/10.1109\/TCSVT.2021.3089480","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"3710_CR7","doi-asserted-by":"publisher","first-page":"4202","DOI":"10.1109\/TCSVT.2021.3127007","volume":"32","author":"Q Zheng","year":"2022","unstructured":"Zheng, Q., Zhu, J., Li, Z.: Collaborative unsupervised multi-view representation learning. IEEE Trans. Circuits Syst. Video Technol. 32, 4202\u20134210 (2022). https:\/\/doi.org\/10.1109\/TCSVT.2021.3127007","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"3710_CR8","doi-asserted-by":"publisher","first-page":"49669","DOI":"10.1109\/ACCESS.2019.2910322","volume":"7","author":"T Shu","year":"2019","unstructured":"Shu, T., Zhang, B., Tang, Y.Y.: Multi-view classification via a fast and effective multi-view nearest-subspace classifier. IEEE Access 7, 49669\u201349679 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2910322","journal-title":"IEEE Access"},{"key":"3710_CR9","doi-asserted-by":"publisher","unstructured":"Wang, X., Fang, J., Zeng, N., Huang, J., Miao, H., Kwapong, W.R., Zhang, Z., Zhang, S., Liu, J.: Reassembling consistent-complementary constraints in triplet network for multi-view learning of medical images. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1235\u20131240. IEEE, Las Vegas, NV, USA (2022). https:\/\/doi.org\/10.1109\/BIBM55620.2022.9995213","DOI":"10.1109\/BIBM55620.2022.9995213"},{"key":"3710_CR10","doi-asserted-by":"publisher","unstructured":"Ren, Z., Zhu, D., Quan, X.: A multi-view 3d reconstruction method that integrates patchmatch and efficient channel attention cascades. In: 2023 5th International Conference on Frontiers Technology of Information and Computer (ICFTIC), pp. 811\u2013814. IEEE, Qiangdao, China (2023). https:\/\/doi.org\/10.1109\/ICFTIC59930.2023.10456051","DOI":"10.1109\/ICFTIC59930.2023.10456051"},{"key":"3710_CR11","doi-asserted-by":"publisher","first-page":"2735","DOI":"10.1109\/TCYB.2019.2934823","volume":"51","author":"H Guo","year":"2021","unstructured":"Guo, H., Sheng, B., Li, P., Chen, C.L.P.: Multiview high dynamic range image synthesis using fuzzy broad learning system. IEEE Trans. Cybern. 51, 2735\u20132747 (2021). https:\/\/doi.org\/10.1109\/TCYB.2019.2934823","journal-title":"IEEE Trans. Cybern."},{"key":"3710_CR12","doi-asserted-by":"publisher","first-page":"2455","DOI":"10.1109\/TCSVT.2021.3079900","volume":"32","author":"B Chen","year":"2022","unstructured":"Chen, B., Zhang, Z., Li, Y., Lu, G., Zhang, D.: Multi-label chest x-ray image classification via semantic similarity graph embedding. IEEE Trans. Circuits Syst. Video Technol. 32, 2455\u20132468 (2022). https:\/\/doi.org\/10.1109\/TCSVT.2021.3079900","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"3710_CR13","doi-asserted-by":"publisher","first-page":"1848","DOI":"10.1109\/TCSVT.2021.3083978","volume":"32","author":"Z Wang","year":"2022","unstructured":"Wang, Z., Fang, Z., Li, D., Yang, H., Du, W.: Semantic supplementary network with prior information for multi-label image classification. IEEE Trans. Circuits Syst. Video Technol. 32, 1848\u20131859 (2022). https:\/\/doi.org\/10.1109\/TCSVT.2021.3083978","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"3710_CR14","doi-asserted-by":"publisher","unstructured":"Zhang, M., Li, C., Wang, X.: Multi-view metric learning for multi-label image classification. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 2134\u20132138. IEEE, Taipei, Taiwan (2019). https:\/\/doi.org\/10.1109\/ICIP.2019.8803160","DOI":"10.1109\/ICIP.2019.8803160"},{"key":"3710_CR15","doi-asserted-by":"publisher","first-page":"2682","DOI":"10.1109\/TPAMI.2020.2974203","volume":"43","author":"S Sun","year":"2021","unstructured":"Sun, S., Zong, D.: LCBM: a multi-view probabilistic model for multi-label classification. IEEE Trans. Pattern Anal. Mach. Intell. 43, 2682\u20132696 (2021). https:\/\/doi.org\/10.1109\/TPAMI.2020.2974203","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3710_CR16","doi-asserted-by":"publisher","first-page":"100979","DOI":"10.1109\/ACCESS.2019.2930468","volume":"7","author":"J Huang","year":"2019","unstructured":"Huang, J., Qu, X., Li, G., Qin, F., Zheng, X., Huang, Q.: Multi-view multi-label learning with view-label-specific features. IEEE Access 7, 100979\u2013100992 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2930468","journal-title":"IEEE Access"},{"key":"3710_CR17","doi-asserted-by":"publisher","first-page":"648","DOI":"10.1109\/TETCI.2023.3235374","volume":"7","author":"S Yang","year":"2023","unstructured":"Yang, S., Lian, C., Zeng, Z., Xu, B., Zang, J., Zhang, Z.: A multi-view multi-scale neural network for multi-label ECG classification. IEEE Trans. Emerg. Topics Comput. Intell. 7, 648\u2013660 (2023). https:\/\/doi.org\/10.1109\/TETCI.2023.3235374","journal-title":"IEEE Trans. Emerg. Topics Comput. Intell."},{"key":"3710_CR18","doi-asserted-by":"publisher","first-page":"1136","DOI":"10.1109\/TSMC.2022.3192635","volume":"53","author":"J Wen","year":"2023","unstructured":"Wen, J., Zhang, Z., Fei, L., Zhang, B., Xu, Y., Zhang, Z., Li, J.: A survey on incomplete multiview clustering. IEEE Trans. Syst. Man Cybern. Syst. 53, 1136\u20131149 (2023). https:\/\/doi.org\/10.1109\/TSMC.2022.3192635","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"3710_CR19","doi-asserted-by":"publisher","first-page":"7955","DOI":"10.1109\/TPAMI.2021.3119334","volume":"44","author":"W Liu","year":"2022","unstructured":"Liu, W., Wang, H., Shen, X., Tsang, I.W.: The emerging trends of multi-label learning. IEEE Trans. Pattern Anal. Mach. Intell. 44, 7955\u20137974 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2021.3119334","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3710_CR20","doi-asserted-by":"publisher","first-page":"5812","DOI":"10.1109\/TIP.2015.2490539","volume":"24","author":"C Xu","year":"2015","unstructured":"Xu, C., Tao, D., Xu, C.: Multi-view learning with incomplete views. IEEE Trans. Image Process. 24, 5812\u20135825 (2015). https:\/\/doi.org\/10.1109\/TIP.2015.2490539","journal-title":"IEEE Trans. Image Process."},{"key":"3710_CR21","doi-asserted-by":"publisher","first-page":"3676","DOI":"10.1109\/TPAMI.2021.3059290","volume":"44","author":"M-K Xie","year":"2022","unstructured":"Xie, M.-K., Huang, S.-J.: Partial multi-label learning with noisy label identification. IEEE Trans. Pattern Anal. Mach. Intell. 44, 3676\u20133687 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2021.3059290","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3710_CR22","doi-asserted-by":"publisher","unstructured":"Liu, J., Teng, S., Zhang, W., Fang, X., Fei, L., Zhang, Z.: Incomplete multi-view subspace clustering with low-rank tensor. In: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3180\u20133184. IEEE, Toronto, ON, Canada (2021). https:\/\/doi.org\/10.1109\/ICASSP39728.2021.9414688","DOI":"10.1109\/ICASSP39728.2021.9414688"},{"key":"3710_CR23","doi-asserted-by":"publisher","first-page":"3033","DOI":"10.1109\/TMM.2022.3154592","volume":"25","author":"P Zhu","year":"2023","unstructured":"Zhu, P., Yao, X., Wang, Y., Cao, M., Hui, B., Zhao, S., Hu, Q.: Latent heterogeneous graph network for incomplete multi-view learning. IEEE Trans. Multimedia 25, 3033\u20133045 (2023). https:\/\/doi.org\/10.1109\/TMM.2022.3154592","journal-title":"IEEE Trans. Multimedia"},{"key":"3710_CR24","doi-asserted-by":"publisher","unstructured":"Liu, W., Feng, S., Tian, H.: Graph-based multi-view partial multi-label learning. In: 2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP), pp. 1\u20135. IEEE, Beijing, China (2022). https:\/\/doi.org\/10.1109\/PAAP56126.2022.10010429","DOI":"10.1109\/PAAP56126.2022.10010429"},{"key":"3710_CR25","doi-asserted-by":"publisher","first-page":"5918","DOI":"10.1109\/TPAMI.2021.3086895","volume":"44","author":"X Li","year":"2022","unstructured":"Li, X., Chen, S.: A concise yet effective model for non-aligned incomplete multi-view and missing multi-label learning. IEEE Trans. Pattern Anal. Mach. Intell. 44, 5918\u20135932 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2021.3086895","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3710_CR26","doi-asserted-by":"publisher","unstructured":"Liu, C., Wen, J., Luo, X., Huang, C., Wu, Z., Xu, Y.: Dicnet: Deep instance-level contrastive network for double incomplete multi-view multi-label classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 8807\u20138815. AAAI Press, Washington, DC, USA (2023). https:\/\/doi.org\/10.1609\/aaai.v37i7.26059","DOI":"10.1609\/aaai.v37i7.26059"},{"key":"3710_CR27","doi-asserted-by":"publisher","unstructured":"Lv, J., Shen, Q., Lv, M., Li, Y., Shi, L., Zhang, P.: Deep learning-based semantic segmentation of remote sensing images: a review. Front. Ecol. Evol. 11, 1201125 (2023) https:\/\/doi.org\/10.3389\/fevo.2023.1201125","DOI":"10.3389\/fevo.2023.1201125"},{"key":"3710_CR28","doi-asserted-by":"publisher","first-page":"30519","DOI":"10.1007\/s11042-022-12821-3","volume":"81","author":"U Sehar","year":"2022","unstructured":"Sehar, U., Naseem, M.L.: How deep learning is empowering semantic segmentation traditional and deep learning techniques for semantic segmentation: A comparison. Multimedia Tools Appl. 81, 30519\u201330544 (2022). https:\/\/doi.org\/10.1007\/s11042-022-12821-3","journal-title":"Multimedia Tools Appl."},{"key":"3710_CR29","doi-asserted-by":"publisher","unstructured":"Zhang, C., Liu, Y., Fu, H.: AE2-NETS: Autoencoder in autoencoder networks. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2572\u20132580. IEEE, Long Beach, CA, USA (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00268","DOI":"10.1109\/CVPR.2019.00268"},{"key":"3710_CR30","doi-asserted-by":"publisher","first-page":"4447","DOI":"10.1109\/TPAMI.2022.3197238","volume":"45","author":"Y Lin","year":"2023","unstructured":"Lin, Y., Gou, Y., Liu, X., Bai, J., Lv, J., Peng, X.: Dual contrastive prediction for incomplete multi-view representation learning. IEEE Trans. Pattern Anal. Mach. Intell. 45, 4447\u20134461 (2023). https:\/\/doi.org\/10.1109\/TPAMI.2022.3197238","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3710_CR31","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1016\/j.neuroimage.2012.03.059","volume":"61","author":"L Yuan","year":"2012","unstructured":"Yuan, L., Wang, Y., Thompson, P.M., Narayan, V.A., Ye, J., Neuroimaging, A.D.: Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data. Neuroimage 61, 622\u2013632 (2012). https:\/\/doi.org\/10.1016\/j.neuroimage.2012.03.059","journal-title":"Neuroimage"},{"key":"3710_CR32","doi-asserted-by":"publisher","first-page":"5559","DOI":"10.1007\/s11276-023-03312-w","volume":"30","author":"A Li","year":"2024","unstructured":"Li, A., Feng, C., Wang, Z., Sun, Y., Wang, Z., Sun, L.: Anchor-based sparse subspace incomplete multi-view clustering. Wireless Netw. 30, 5559\u20135570 (2024). https:\/\/doi.org\/10.1007\/s11276-023-03312-w","journal-title":"Wireless Netw."},{"key":"3710_CR33","doi-asserted-by":"publisher","first-page":"3702","DOI":"10.1109\/TIP.2023.3290527","volume":"32","author":"R Fan","year":"2023","unstructured":"Fan, R., Ouyang, X., Luo, T., Hu, D., Hou, C.: Incomplete multi-view learning under label shift. IEEE Trans. Image Process. 32, 3702\u20133716 (2023). https:\/\/doi.org\/10.1109\/TIP.2023.3290527","journal-title":"IEEE Trans. Image Process."},{"key":"3710_CR34","unstructured":"Zhang, W., Zhang, K., Gu, P., Xue, X.: Multi-view embedding learning for incompletely labeled data. In: International Joint Conference on Artificial Intelligence, pp. 1910\u20131916 (2013)"},{"key":"3710_CR35","doi-asserted-by":"publisher","first-page":"20110","DOI":"10.1007\/s10489-023-04562-z","volume":"53","author":"X Ji","year":"2023","unstructured":"Ji, X., Tan, A., Wu, W.-Z., Gu, S.: Multi-label classification with weak labels by learning label correlation and label regularization. Appl. Intell. 53, 20110\u201320133 (2023). https:\/\/doi.org\/10.1007\/s10489-023-04562-z","journal-title":"Appl. Intell."},{"key":"3710_CR36","doi-asserted-by":"publisher","unstructured":"Li, H., Wang, N., Yang, X., Wang, X., Gao, X.: Towards semi-supervised deep facial expression recognition with an adaptive confidence margin. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4156\u20134165. IEEE, New Orleans, LA, USA (2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.00413","DOI":"10.1109\/CVPR52688.2022.00413"},{"key":"3710_CR37","doi-asserted-by":"publisher","unstructured":"Zhang, X., Abdelfattah, R., Song, Y., Wang, X.: An effective approach for multi-label classification with missing labels. In: 2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC\/DSS\/SmartCity\/DependSys), pp. 1713\u20131720. IEEE, Hainan, China (2022). https:\/\/doi.org\/10.1109\/HPCC-DSS-SmartCity-DependSys57074.2022.00259","DOI":"10.1109\/HPCC-DSS-SmartCity-DependSys57074.2022.00259"},{"key":"3710_CR38","doi-asserted-by":"publisher","unstructured":"Lin, Y., Gou, Y., Liu, Z., Li, B., Lv, J., Peng, X.: Completer: Incomplete multi-view clustering via contrastive prediction. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11169\u201311178. IEEE, Nashville, TN, USA (2021). https:\/\/doi.org\/10.1109\/CVPR46437.2021.01102","DOI":"10.1109\/CVPR46437.2021.01102"},{"key":"3710_CR39","doi-asserted-by":"publisher","unstructured":"Kam\u00a0Ho, T.: Complexity of representations in deep learning. In: 2022 26th International Conference on Pattern Recognition (ICPR), pp. 2657\u20132663. IEEE, Montreal, QC, Canada (2022). https:\/\/doi.org\/10.1109\/ICPR56361.2022.9956594","DOI":"10.1109\/ICPR56361.2022.9956594"},{"key":"3710_CR40","doi-asserted-by":"publisher","unstructured":"Jin, J., Wang, S., Dong, Z., Liu, X., Zhu, E.: Deep incomplete multi-view clustering with cross-view partial sample and prototype alignment. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11600\u201311609. IEEE, Vancouver, BC, Canada (2023). https:\/\/doi.org\/10.1109\/CVPR52729.2023.01116","DOI":"10.1109\/CVPR52729.2023.01116"},{"key":"3710_CR41","doi-asserted-by":"publisher","unstructured":"Duan, Y.Q., Yuan, H.L., Yin, M., Lai, L.L.: Deep multi-view subspace clustering based on intact space learning. In: 2021 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR), pp. 1\u20136. IEEE, Adelaide, Australia (2021). https:\/\/doi.org\/10.1109\/ICWAPR54887.2021.9736153","DOI":"10.1109\/ICWAPR54887.2021.9736153"},{"key":"3710_CR42","doi-asserted-by":"publisher","unstructured":"Xu, J., Tang, H., Ren, Y., Peng, L., Zhu, X., He, L.: Multi-level feature learning for contrastive multi-view clustering. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16030\u201316039. IEEE, New Orleans, LA, USA (2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.01558","DOI":"10.1109\/CVPR52688.2022.01558"},{"key":"3710_CR43","doi-asserted-by":"publisher","unstructured":"Lyc1022: DMVMLC-VT: Deep Incomplete Multi-View Image Classification with View Translation and Pseudo-Label Enhancement. zenodo https:\/\/doi.org\/10.5281\/zenodo.12586781 (2024)","DOI":"10.5281\/zenodo.12586781"},{"key":"3710_CR44","doi-asserted-by":"publisher","unstructured":"Tan, Q., Yu, G., Domeniconi, C., Wang, J., Zhang, Z.: Incomplete multi-view weak-label learning. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18, pp. 2703\u20132709. International Joint Conferences on Artificial Intelligence Organization, Stockholm, Sverige (2018). https:\/\/doi.org\/10.24963\/ijcai.2018\/375","DOI":"10.24963\/ijcai.2018\/375"},{"key":"3710_CR45","doi-asserted-by":"crossref","unstructured":"Duygulu, P., Barnard, K., Freitas, J., Forsyth, D.: Object recognition as machine translation: Learning a lexicon for a fixed imago vocabulary. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) COMPUTER VISION - ECCV 2002, PT IV, vol. 2353, pp. 97\u2013112. DENMARK, COPENHAGEN (2002)","DOI":"10.1007\/3-540-47979-1_7"},{"key":"3710_CR46","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. Int. J. Comput. Vision 88, 303\u2013338 (2010). https:\/\/doi.org\/10.1007\/s11263-009-0275-4","journal-title":"Int. J. Comput. Vision"},{"key":"3710_CR47","doi-asserted-by":"publisher","unstructured":"Ahn, L., Dabbish, L.: Labeling images with a computer game. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 319\u2013326. Association for Computing Machinery, New York, NY, USA (2004). https:\/\/doi.org\/10.1145\/985692.985733","DOI":"10.1145\/985692.985733"},{"key":"3710_CR48","unstructured":"Grubinger, M., Clough, P.D., M\u00fcller, H., Deselaers, T.: The iapr tc12 benchmark: A new evaluation resource for visual information systems. Proceedings of the International Conference on Language Resources and Evaluation, 1\u201311 (2006)"},{"key":"3710_CR49","doi-asserted-by":"publisher","unstructured":"Huiskes, M.J., Lew, M.S.: The mir flickr retrieval evaluation. In: Proceedings of the 1st ACM International Conference on Multimedia Information Retrieval, pp. 39\u201343. Association for Computing Machinery, New York, NY, USA (2008). https:\/\/doi.org\/10.1145\/1460096.1460104","DOI":"10.1145\/1460096.1460104"},{"key":"3710_CR50","doi-asserted-by":"publisher","first-page":"1819","DOI":"10.1109\/TKDE.2013.39","volume":"26","author":"M-L Zhang","year":"2014","unstructured":"Zhang, M.-L., Zhou, Z.-H.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26, 1819\u20131837 (2014). https:\/\/doi.org\/10.1109\/TKDE.2013.39","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"3710_CR51","doi-asserted-by":"publisher","unstructured":"Bucak, S.S., Jin, R., Jain, A.K.: Multi-label learning with incomplete class assignments. In: CVPR 2011, pp. 2801\u20132808. IEEE, Colorado Springs, CO, USA (2011). https:\/\/doi.org\/10.1109\/CVPR.2011.5995734","DOI":"10.1109\/CVPR.2011.5995734"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03710-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-024-03710-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03710-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T10:01:48Z","timestamp":1745488908000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-024-03710-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,16]]},"references-count":51,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["3710"],"URL":"https:\/\/doi.org\/10.1007\/s00371-024-03710-x","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,16]]},"assertion":[{"value":"30 October 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 November 2024","order":2,"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"}}]}}