{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T17:09:23Z","timestamp":1776100163349,"version":"3.50.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2020,10,30]],"date-time":"2020-10-30T00:00:00Z","timestamp":1604016000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,10,30]],"date-time":"2020-10-30T00:00:00Z","timestamp":1604016000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,2]]},"DOI":"10.1007\/s11042-020-09965-5","type":"journal-article","created":{"date-parts":[[2020,10,30]],"date-time":"2020-10-30T19:02:26Z","timestamp":1604084546000},"page":"7789-7803","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Text to image synthesis using multi-generator text conditioned generative adversarial networks"],"prefix":"10.1007","volume":"80","author":[{"given":"Min","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Chunye","family":"Li","sequence":"additional","affiliation":[]},{"given":"Zhiping","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,30]]},"reference":[{"key":"9965_CR1","unstructured":"Arjovsky M, Chintala S, Bottou L (2017) Wasserstein generative adversarial networks. In: Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017, pp 214\u2013223"},{"key":"9965_CR2","unstructured":"Bang D, Shim H (2018) MGGAN: solving mode collapse using manifold guided training, arXiv:1804.04391"},{"key":"9965_CR3","unstructured":"Bishop CM (2006) Pattern recognition and machine learning (information science and statistics)"},{"key":"9965_CR4","unstructured":"Bodnar C (2018) Text to image synthesis using generative adversarial networks, arXiv:1805.00676"},{"key":"9965_CR5","unstructured":"Che T, Li Y, Jacob AP, Bengio Y, Li W (2016) Mode regularized generative adversarial networks, arXiv:1612.02136"},{"key":"9965_CR6","unstructured":"Chidambaram M, Qi Y (2017) Style transfer generative adversarial networks: Learning to play chess differently, arXiv:1702.06762"},{"key":"9965_CR7","unstructured":"Dash A, Gamboa JCB, Ahmed S, Liwicki M, Afzal MZ (2017) TAC-GAN - text conditioned auxiliary classifier generative adversarial network, arXiv:1703.06412"},{"key":"9965_CR8","unstructured":"Ghosh A, Kulharia V, Namboodiri VP, Torr PHS, Dokania PK (2018) Multi-agent diverse generative adversarial networks. In: 2018 IEEE Conference on computer vision and pattern recognition, CVPR 2018. Salt Lake City, UT, USA, June 18-22, 2018, pp 8513\u20138521"},{"key":"9965_CR9","unstructured":"Goodfellow IJ, Pouget-abadie J, Mirza M, Xu B, Warde-farley D, Ozair S, Courville AC, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems 27: Annual conference on neural information processing systems 2014, December 8-13 2014, Montreal, Quebec, Canada, pp 2672\u20132680"},{"issue":"3","key":"9965_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1142\/S021800141859005X","volume":"32","author":"H Guo","year":"2018","unstructured":"Guo H, Han L, Su S, Sun Z (2018) Deep multi-instance multi-label learning for image annotation. Int J Pattern Recognit Artif Intell 32(3):1\u201316","journal-title":"Int J Pattern Recognit Artif Intell"},{"issue":"99","key":"9965_CR11","first-page":"1","volume":"PP","author":"Z Han","year":"2017","unstructured":"Han Z, Tao X, Li H, Zhang S, Wang X, Huang X, Metaxas D (2017) Stackgan++: Realistic image synthesis with stacked generative adversarial networks. IEEE Trans Pattern Anal & Machine Intell PP(99):1\u20131","journal-title":"IEEE Trans Pattern Anal & Machine Intell"},{"key":"9965_CR12","unstructured":"Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S (2017) Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Advances in neural information processing systems 30: Annual conference on neural information processing systems 2017, 4-9 December 2017, Long Beach, CA, USA, pp 6629\u20136640"},{"key":"9965_CR13","doi-asserted-by":"crossref","unstructured":"Li J, Monroe W, Shi T, Jean S, Ritter A, Jurafsky D (2017) Adversarial learning for neural dialogue generation. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, September 9-11, 2017,Copenhagen, Denmark, pp 2157\u20132169","DOI":"10.18653\/v1\/D17-1230"},{"key":"9965_CR14","unstructured":"Lin Z, Khetan A, Fanti GC, Oh S (2018) Pacgan: The power of two samples in generative adversarial networks. In: Advances in neural information processing systems 31: Annual conference on neural information processing systems 2018, neurIPS 2018, 3-8 December 2018, Montr\u00e9al, Canada, pp 1505\u20131514"},{"key":"9965_CR15","doi-asserted-by":"crossref","unstructured":"Lu X, Ma C, Ni B, Yang X (2019) Adaptive region proposal with channel regularization for robust object tracking. IEEE Trans Circuits Syst Video Technol, pp 69\u201382","DOI":"10.1109\/TCSVT.2019.2944654"},{"key":"9965_CR16","unstructured":"Mansimov E, Parisotto E, Ba LJ, Salakhutdinov R (2015) Generating images from captions with attention, arXiv:1511.02793"},{"key":"9965_CR17","unstructured":"Metz L, Poole B, Pfau D, Sohl-dickstein J (2016) Unrolled generative adversarial networks. arXiv:1611.02163"},{"key":"9965_CR18","unstructured":"Mirza M, Osindero S (2014) Conditional generative adversarial nets. Computer Science, pp 2672\u20132680"},{"key":"9965_CR19","unstructured":"Moradshahi M, Contractor U (2018) Language modeling with generative adversarialnetworks"},{"key":"9965_CR20","unstructured":"Nilsback M, Zisserman A (2008) Automated flower classification over a large number of classes. In: Sixth indian conference on computer vision, graphics & image processing, ICVGIP 2008. Bhubaneswar, India, 16-19 December 2008, pp 722\u2013729"},{"key":"9965_CR21","unstructured":"Odena A, Olah C, Shlens J (2017) Conditional image synthesis with auxiliary classifier gans. In: Proceedings of the 34th International Conference on Machine Learning, ICML 2017. Sydney, NSW, Australia, 6-11 August 2017, pp 2642\u20132651"},{"key":"9965_CR22","unstructured":"van den Oord A, Kalchbrenner N, Espeholt L, Kavukcuoglu K, Vinyals O, Graves A (2016) Conditional image generation with pixelcnn decoders. In: Advances in neural information processing systems 29: Annual conference on neural information processing systems 2016, December 5-10, 2016, Barcelona, Spain, pp 4790\u20134798"},{"key":"9965_CR23","unstructured":"Welinder P, Branson S, Mita T, Wah C, Schroff F, Branson S, Perona P Caltech-ucsd birds-200-2010 pp 2,5"},{"key":"9965_CR24","unstructured":"Reed S, Akata Z, Yan X, Logeswaran L, Schiele B, Lee H (2016) Generative adversarial text to image synthesis. In: International conference on international conference on machine learning, pp 2,3,5,7,8,9"},{"key":"9965_CR25","unstructured":"Reed SE, Akata Z, Mohan S, Tenka S, Schiele B, Lee H (2016) Learning what and where to draw. In: Advances in neural information processing systems 29: Annual conference on neural information processing systems 2016. December 5-10, 2016, Barcelona, Spain, pp 217\u2013225"},{"key":"9965_CR26","unstructured":"Reed SE, Akata Z, Yan X, Logeswaran L, Schiele B, Lee H (2016) Generative adversarial text to image synthesis. In: Proceedings of the 33nd International Conference on Machine Learning, ICML 2016. New York City, NY, USA, June 19-24, 2016, pp 1060\u20131069"},{"key":"9965_CR27","unstructured":"Salimans T, Goodfellow IJ, Zaremba W, Cheung V, Radford A, Chen X (2016) Improved techniques for training gans. In: Advances in neural information processing systems 29: Annual conference on neural information processing systems 2016. December 5-10, 2016, Barcelona, Spain, pp 2226\u20132234"},{"key":"9965_CR28","unstructured":"Song Y, Ma C, Wu X, Gong L, Bao L, Zuo W, Shen C, Lau RWH, Yang M. (2018) VITAL: visual tracking via adversarial learning. In: 2018 IEEE Conference on computer vision and pattern recognition, CVPR 2018. Salt Lake City, UT, USA, June 18-22, 2018. IEEE Computer Society, pp 8990\u20138999"},{"key":"9965_CR29","unstructured":"Srivastava A, Valkov L, Russell C, Gutmann MU, Sutton CA (2017) VEEGAN: reducing mode collapse in gans using implicit variational learning. In: Advances in neural information processing systems 30: Annual conference on neural information processing systems 2017, 4-9 December 2017, Long Beach, CA, USA, pp 3310\u20133320"},{"key":"9965_CR30","unstructured":"Thanh-Tung H, Tran T, Venkatesh S (2018) On catastrophic forgetting and mode collapse in generative adversarial networks, arXiv:1807.04015"},{"key":"9965_CR31","unstructured":"Xiang S, Li H (2017) On the effects of batch and weight normalization in generative adversarial networks"},{"key":"9965_CR32","doi-asserted-by":"crossref","unstructured":"Xu C, Cui Y, Zhang Y, Gao P, Xu J (2019) Person-independent facial expression recognition method based on improved wasserstein generative adversarial networks in combination with identity aware. Multimedia Systems","DOI":"10.1007\/s00530-019-00628-6"},{"key":"9965_CR33","doi-asserted-by":"crossref","unstructured":"Zhang H, Xu T, Li H (2017) Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. In: IEEE International conference on computer vision, ICCV 2017, Venice, Italy, October 22-29, 2017, pp 5908\u20135916","DOI":"10.1109\/ICCV.2017.629"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09965-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11042-020-09965-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09965-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,24]],"date-time":"2021-02-24T23:53:38Z","timestamp":1614210818000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11042-020-09965-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,30]]},"references-count":33,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,2]]}},"alternative-id":["9965"],"URL":"https:\/\/doi.org\/10.1007\/s11042-020-09965-5","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,30]]},"assertion":[{"value":"21 May 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 August 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 September 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 October 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of interests"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Ethical approval"}}]}}