{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T16:02:58Z","timestamp":1740153778604,"version":"3.37.3"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T00:00:00Z","timestamp":1724976000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T00:00:00Z","timestamp":1724976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Comput"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s12559-024-10336-7","type":"journal-article","created":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T08:03:45Z","timestamp":1725005025000},"page":"2916-2930","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Multimodal Generative Learning Model based on Basic Fuzzy Concepts"],"prefix":"10.1007","volume":"16","author":[{"given":"Huankun","family":"Sheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4369-6856","authenticated-orcid":false,"given":"Hongwei","family":"Mo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tengteng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,30]]},"reference":[{"key":"10336_CR1","unstructured":"Kingma DP, Rezende DJ, Mohamed S, Welling M. Semi-supervised learning with deep generative models. In: Advances in neural information processing systems; 2014. pp. 3581\u20133589."},{"key":"10336_CR2","unstructured":"Kulkarni TD, Whitney W, Kohli P, Tenenbaum JB. Deep convolutional inverse graphics network. In: Advances in neural information processing systems; 2015.pp. 2539\u20132547."},{"key":"10336_CR3","doi-asserted-by":"crossref","unstructured":"Yan X, Yang J, Sohn K, Lee H. Attribute2image: conditional image generation from visual attributes. In: European conference on computer vision; 2016. pp. 776\u2013791.","DOI":"10.1007\/978-3-319-46493-0_47"},{"key":"10336_CR4","unstructured":"Larsen ABL, S\u00f8nderby SK, Larochelle H, Winther O. Autoencoding beyond pixels using a learned similarity metric.arXiv preprint. 2015. arXiv:1512.09300."},{"key":"10336_CR5","doi-asserted-by":"crossref","unstructured":"Li Y, Ouyang W, Zhou B, Shi J, Zhang C, Wang X. Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph Generation. In: European conference on computer vision; 2018. Pp. 335\u2013351.","DOI":"10.1007\/978-3-030-01246-5_21"},{"key":"10336_CR6","doi-asserted-by":"crossref","unstructured":"Yang J, Lu J, Lee S, Batra D, Parikh D. Graph R-CNN for scene graph generation. In: European conference on computer vision; 2018. pp. 670\u2013685.","DOI":"10.1007\/978-3-030-01246-5_41"},{"key":"10336_CR7","doi-asserted-by":"crossref","unstructured":"Xu D, Zhu Y, Choy CB, Fei-Fei L. Scene graph generation by iterative message passing. In: IEEE Conference on computer vision and pattern recognition (CVPR); 2017. pp. 5410\u20135419.","DOI":"10.1109\/CVPR.2017.330"},{"key":"10336_CR8","doi-asserted-by":"crossref","unstructured":"Johnson J, Gupta A, Fei-Fei L. Image generation from scene graphs. In: IEEE Conference on computer vision and pattern recognition; 2018 pp.1219\u20131228.","DOI":"10.1109\/CVPR.2018.00133"},{"key":"10336_CR9","doi-asserted-by":"publisher","first-page":"763","DOI":"10.1007\/s12559-018-9581-x","volume":"11","author":"G Ding","year":"2019","unstructured":"Ding G, Chen M, Zhao S, et al. Neural image caption generation with weighted training and reference. Cogn comput. 2019;11:763\u201377.","journal-title":"Cogn comput."},{"key":"10336_CR10","unstructured":"Sohn K, Shang W, Lee H. Improved Multimodal Deep Learning with Variation of Information. In: Advances in neural information processing systems; 2014. pp. 2141\u20132149."},{"key":"10336_CR11","unstructured":"Kingma DP, Welling M. Auto-Encoding variational bayes. arXiv preprint. 2013. arXiv:1312.6114."},{"key":"10336_CR12","unstructured":"Rezende DJ, Mohamed S, Wierstra D. Stochastic backpropagation and variational inference in deep latent gaussian models. arXiv preprint. 2014. arXiv:1401.4082."},{"key":"10336_CR13","doi-asserted-by":"crossref","unstructured":"Wang W, Yan X, Lee H, Livescu K. Deep variational canonical correlation analysis.arXiv preprint. 2016. arXiv:1610.03454.","DOI":"10.21437\/Interspeech.2017-1581"},{"key":"10336_CR14","unstructured":"Suzuki M, Nakayama K, Matsuo Y. Joint multimodal learning with deep generative models. arXiv preprint. 2016. arXiv:1611.01891."},{"key":"10336_CR15","unstructured":"Vedantam R, Fischer I, Huang J, Murphy K. Generative models of visually grounded imagination. arXiv preprint. 2018. arXiv:1705.10762."},{"key":"10336_CR16","unstructured":"Higgins I, Sonnerat N, Matthey L, Pal A, Burgess CP, Botvinick M, et al. Scan: learning abstract hierarchical compositional visual concepts; arXiv preprint. 2017. arXiv:1707.03389."},{"key":"10336_CR17","doi-asserted-by":"crossref","unstructured":"Liu C, Shang Z, Tang YY. Zero-Shot Learning with Fuzzy Attribute. In: 2017 3rd IEEE international conference on cybernetics (CYBCONF); 2017. pp. 1\u20136.","DOI":"10.1109\/CYBConf.2017.7985823"},{"issue":"18","key":"10336_CR18","doi-asserted-by":"publisher","first-page":"1539","DOI":"10.1016\/j.artint.2009.07.006","volume":"173","author":"J Lawry","year":"2009","unstructured":"Lawry J, Tang Y. Uncertainty modelling for vague concepts: a prototype theory approach. Artif Intell. 2009;173(18):1539\u201358.","journal-title":"Artif Intell"},{"issue":"3","key":"10336_CR19","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1007\/s12559-018-9544-2","volume":"10","author":"XH Li","year":"2018","unstructured":"Li XH, Chen XH. D-Intuitionistic hesitant fuzzy sets and their application in multiple attribute decision making. Cogn Comput. 2018;10(3):496\u2013505.","journal-title":"Cogn Comput"},{"issue":"4","key":"10336_CR20","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1007\/s12559-017-9453-9","volume":"9","author":"PD Liu","year":"2017","unstructured":"Liu PD, Li HG. Interval-valued intuitionistic fuzzy power bonferroni aggregation operators and their application to group decision making. Cogn Comput. 2017;9(4):494\u2013512.","journal-title":"Cogn Comput"},{"issue":"1","key":"10336_CR21","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1007\/s12559-019-09701-8","volume":"12","author":"H Seiti","year":"2020","unstructured":"Seiti H, Hafezalkotob A. A New risk-based fuzzy cognitive model and its application to decision-making. Cogn Comput. 2020;12(1):309\u201326.","journal-title":"Cogn Comput"},{"issue":"1","key":"10336_CR22","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/s12559-018-9597-2","volume":"11","author":"P Liu","year":"2019","unstructured":"Liu P, Qin X. A new decision-making method based on interval-valued linguistic intuitionistic fuzzy information. Cogn Comput. 2019;11(1):125\u201344.","journal-title":"Cogn Comput"},{"key":"10336_CR23","unstructured":"Chen RTQ, Li X, Grosse R, Duvenaud D. Isolating sources of disentanglement in variational autoencoders. In: Advances in neural information processing systems. 2018. pp. 2610\u20132620."},{"issue":"8","key":"10336_CR24","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2012","unstructured":"Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell. 2012;35(8):1798\u2013828.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10336_CR25","doi-asserted-by":"crossref","unstructured":"Zhu YZ, Min MR, Kadav A, et al. S3VAE: self-supervised sequential VAE for representation disentanglement and data generation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition; 2020. pp. 6538\u20136547.","DOI":"10.1109\/CVPR42600.2020.00657"},{"issue":"3","key":"10336_CR26","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1016\/S0019-9958(65)90241-X","volume":"8","author":"LA Zadeh","year":"1965","unstructured":"Zadeh LA. Fuzzy sets. Inf Control. 1965;8(3):338\u201353.","journal-title":"Inf Control"},{"issue":"3","key":"10336_CR27","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1037\/0096-3445.104.3.192","volume":"104","author":"E Roche","year":"1975","unstructured":"Roche E. Cognitive representations of semantic categories. J Exp Psychol-Gen. 1975;104(3):192.","journal-title":"J Exp Psychol-Gen"},{"key":"10336_CR28","unstructured":"Goodman IR, Nguyen HT. Uncertainty models for knowledge-based systems: a unified approach to the measurement of uncertainty, Elsevier Science Inc. 1985."},{"issue":"2","key":"10336_CR29","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/S0165-0114(97)00080-8","volume":"90","author":"D Dubois","year":"1997","unstructured":"Dubois D, Prade H. The three semantics of fuzzy sets. Fuzzy Sets Syst. 1997;90(2):141\u201350.","journal-title":"Fuzzy Sets Syst"},{"key":"10336_CR30","doi-asserted-by":"crossref","unstructured":"Lawry J, Tang Y. Relating Prototype Theory and Label Semantics. Soft Methods for Handling Variability and Imprecision.Springer; 2008. pp. 35\u201342.","DOI":"10.1007\/978-3-540-85027-4_5"},{"key":"10336_CR31","doi-asserted-by":"crossref","unstructured":"Tang Y, Lawry J. Information cell mixture models: The cognitive representations of vague concepts. Integrated Uncertainty Management and Applications, Springer; 2010. pp. 371\u2013382.","DOI":"10.1007\/978-3-642-11960-6_35"},{"key":"10336_CR32","unstructured":"Sohn K, Yan X, Lee H, Arbor A. Learning structured output representation using deep conditional generative models. In: Advances in neural information processing systems; 2015. pp. 83\u20133491."},{"key":"10336_CR33","doi-asserted-by":"crossref","unstructured":"Pandey G, Dukkipati A. Variational methods for conditional multimodal deep learning. In: International joint conference on neural networks; 2017. pp. 308\u2013315.","DOI":"10.1109\/IJCNN.2017.7965870"},{"key":"10336_CR34","doi-asserted-by":"crossref","unstructured":"Wang X, Tan K, Du Q, et al. CVA2E: A conditional variation-al autoencoder with an adversarial training process for hyperspectral imagery classification. IEEE Transactions on geoscience and remote sensing; 2020. pp. 1\u201317.","DOI":"10.1109\/TGRS.2020.2968304"},{"key":"10336_CR35","doi-asserted-by":"crossref","unstructured":"Wu H, Jia J, Xie L, et al. Cross-VAE: Towards disentangling expression from identity for human faces. In: IEEE International conference on acoustics, speech and signal processing (ICASSP); 2020. pp. 4087\u20134091.","DOI":"10.1109\/ICASSP40776.2020.9053608"},{"key":"10336_CR36","doi-asserted-by":"crossref","unstructured":"Goodfellow I. J., Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A ,Bengio Y. Generative adversarial nets. In: Advances in neural information processing systems. 2014. pp. 139\u201344.","DOI":"10.1145\/3422622"},{"key":"10336_CR37","unstructured":"Reed S, Akata Z, Mohan S, Tenka S, Schiele B, Lee H. Learning what and where to draw. In: Advances in neural information processing systems; 2016. pp. 217\u2013225."},{"key":"10336_CR38","unstructured":"Reed S, Akata Z, Yan X, Logeswaran L, Schiele B, Lee H. Generative adversarial text to image synthesis. In: International conference on machine learning. 2016. pp: 1060\u20139."},{"key":"10336_CR39","doi-asserted-by":"crossref","unstructured":"Heim E. Constrained generative adversarial networks for interactive image generation. In: The IEEE conference on computer vision and pattern recognition (CVPR); 2019. pp. 10753\u201310761.","DOI":"10.1109\/CVPR.2019.01101"},{"key":"10336_CR40","doi-asserted-by":"crossref","unstructured":"Park T, Liu MY, Wang TC, Zhu JY. Semantic image synthesis with spatially-adaptive normalization. In: The IEEE conference on computer vision and pattern recognition (CVPR); 2019 pp. 2337\u20132346.","DOI":"10.1109\/CVPR.2019.00244"},{"key":"10336_CR41","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s12559-019-09670-y","volume":"12","author":"Z Wang","year":"2020","unstructured":"Wang Z, Healy G, Smeaton AF, et al. Use of neural signals to evaluate the quality of generative adversarial network performance in facial image generation. Cogn Comput. 2020;12:13\u201324.","journal-title":"Cogn Comput"},{"key":"10336_CR42","doi-asserted-by":"crossref","unstructured":"Gu JX, Zhao HD, Lin Z, Li S, Cai JF, Ling . Scene graph generation with external knowledge and image reconstruction. In: The IEEE conference on computer vision and pattern recognition (CVPR); 2019. pp. 1969\u20131978.","DOI":"10.1109\/CVPR.2019.00207"},{"key":"10336_CR43","doi-asserted-by":"crossref","unstructured":"Zakraoui J, Saleh M, Asghar U, et al. Generating images from Arabic story-text using scene graph, In: 2020 IEEE international conference on informatics, IoT, and enabling technologies (ICIoT); 2020. pp. 469\u2013475.","DOI":"10.1109\/ICIoT48696.2020.9089495"},{"key":"10336_CR44","unstructured":"Tenenbaum J. Building machines that learn and think like people. In: Proceedings of the 17th international conference on autonomous agents and multiAgent systems. 2018. p. 5."},{"issue":"6266","key":"10336_CR45","doi-asserted-by":"publisher","first-page":"1332","DOI":"10.1126\/science.aab3050","volume":"350","author":"BM Lake","year":"2015","unstructured":"Lake BM, Salakhutdinov R, Tenenbaum JB. Human-level concept learning through probabilistic program induction. Science. 2015;350(6266):1332\u20138.","journal-title":"Science"},{"issue":"1","key":"10336_CR46","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/s12559-017-9507-z","volume":"10","author":"SF Zhang","year":"2018","unstructured":"Zhang SF, Huang KZ, Zhang R, Hussain A. Learning from few samples with memory network. Cogn Comput. 2018;10(1):15\u201322.","journal-title":"Cogn Comput"},{"key":"10336_CR47","unstructured":"Huang WM, Xu YD. Realistic image generation using region-phrase attention. arXiv preprint. 2019. arXiv:1902.05395."},{"key":"10336_CR48","unstructured":"Harris E, Niranjan M, Hare J. A biologically inspired visual working memory for deep networks.arXiv preprint. 2019. arXiv:1901.03665."},{"key":"10336_CR49","unstructured":"Gauthier J, Levy R, Tenenbaum J. B. Word learning and the acquisition of syntactic-semantic overhypotheses. In: CogSci. 2018. pp. 1699\u2013704."},{"key":"10336_CR50","unstructured":"Mao J, Gan C, Kohli P, Tenenbaum JB, Wu J. The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision. arXiv preprint. 2019. arXiv:1904.12584."},{"key":"10336_CR51","unstructured":"Yi K, Wu J, Gan C, Torralba A, Kohli P, Tenenbaum J. B. Neural-symbolic vqa: disentangling reasoning from vision and language understanding. In: Proceedings of the 32nd international conference on neural information processing systems. 2018. pp. 1039\u201350."},{"key":"10336_CR52","unstructured":"Higgins I, Matthey L, Glorot X, Pal A, et al. beta-VAE: Learning basic visual concepts with a constrained variational framework. In: International conference on learning representations; 2017. p. 3."},{"key":"10336_CR53","unstructured":"Matthey L, Higgins I, Hassabis D, Lerchner A. dsprites: Disentanglement testing sprites dataset. 2017. https:\/\/github.com\/deepmind\/dsprites-dataset. Accesed 2 Oct 2017."},{"key":"10336_CR54","unstructured":", Kingma D, Adam JB. A method for stochastic optimization. arXiv preprint. 2014. arXiv:1412.6980."},{"key":"10336_CR55","doi-asserted-by":"crossref","unstructured":"Aubry M, Maturana D, Efros AA, Russell BC and Sivic J. Seeing 3D Chairs: Exemplar part-based 2D-3D alignment using a large dataset of cad models. In: IEEE Conference on computer vision and pattern recognition; 2014. pp. 3762\u20133769.","DOI":"10.1109\/CVPR.2014.487"}],"container-title":["Cognitive Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-024-10336-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12559-024-10336-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-024-10336-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T09:46:30Z","timestamp":1730972790000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12559-024-10336-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,30]]},"references-count":55,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["10336"],"URL":"https:\/\/doi.org\/10.1007\/s12559-024-10336-7","relation":{},"ISSN":["1866-9956","1866-9964"],"issn-type":[{"type":"print","value":"1866-9956"},{"type":"electronic","value":"1866-9964"}],"subject":[],"published":{"date-parts":[[2024,8,30]]},"assertion":[{"value":"18 April 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 August 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":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"The authors declare that they have no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}