{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T17:13:07Z","timestamp":1777655587158,"version":"3.51.4"},"reference-count":47,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,3,1]],"date-time":"2020-03-01T00:00:00Z","timestamp":1583020800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,3,1]],"date-time":"2020-03-01T00:00:00Z","timestamp":1583020800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,3,1]],"date-time":"2020-03-01T00:00:00Z","timestamp":1583020800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,3]]},"DOI":"10.1109\/wacv45572.2020.9093610","type":"proceedings-article","created":{"date-parts":[[2020,5,14]],"date-time":"2020-05-14T23:41:09Z","timestamp":1589499669000},"page":"863-872","source":"Crossref","is-referenced-by-count":31,"title":["CANZSL: Cycle-Consistent Adversarial Networks for Zero-Shot Learning from Natural Language"],"prefix":"10.1109","author":[{"given":"Zhi","family":"Chen","sequence":"first","affiliation":[]},{"given":"Jingjing","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yadan","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Zi","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Yangyang","family":"Yangyang","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00581"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2878970"},{"key":"ref33","first-page":"2152","article-title":"An embarrassingly simple approach to zero-shot learning","author":"romera-paredes","year":"2015","journal-title":"International Conference on Machine Learning"},{"key":"ref32","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"radford","year":"2015","journal-title":"arXiv preprint arXiv 1511 06434"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.247"},{"key":"ref30","first-page":"2642","article-title":"Conditional image synthesis with auxiliary classifier gans","author":"odena","year":"2017","journal-title":"Proceedings of the 34th International Conference on Machine Learning-Volume 70"},{"key":"ref37","article-title":"The Caltech-UCSD Birds-200-2011 Dataset","author":"wah","year":"2011","journal-title":"Technical Report CNS-TR-2011-001"},{"key":"ref36","first-page":"595","article-title":"Building a bird recognition app and large scale dataset with citizen scientists","author":"van horn","year":"2015","journal-title":"The fine print in fine-grained dataset collection"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/0306-4573(88)90021-0"},{"key":"ref34","first-page":"2234","article-title":"Improved techniques for training gans","author":"salimans","year":"2016","journal-title":"Advances in neural information processing systems"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2643667"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00143"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.321"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.666"},{"key":"ref13","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Advances in neural information processing systems"},{"key":"ref14","first-page":"5767","article-title":"Improved training of wasserstein gans","author":"gulrajani","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref15","author":"guo","year":"2017","journal-title":"Synthesizing samples fro zero-shot learning"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2696747"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"hinton","year":"2006","journal-title":"Science"},{"key":"ref18","article-title":"Cycada: Cycle-consistent adversarial domain adaptation","author":"hoffman","year":"2017","journal-title":"arXiv preprint arXiv 1711 03890"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.453"},{"key":"ref28","article-title":"Conditional generative adversarial nets","author":"mirza","year":"2014","journal-title":"arXiv preprint arXiv 1411 1784"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.575"},{"key":"ref27","article-title":"Multi-class generative adversarial networks with the l2 loss function","volume":"5","author":"mao","year":"2016","journal-title":"arXiv preprint ArXiv 1611 04076"},{"key":"ref3","article-title":"Wasserstein gan","author":"arjovsky","year":"2017","journal-title":"arXiv preprint arXiv 1701 07875"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00115"},{"key":"ref29","article-title":"Zero-shot learning by convex combination of semantic embeddings","author":"norouzi","year":"2013","journal-title":"arXiv preprint arXiv 1312 5650"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_4"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICMEW.2019.00085"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.64"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298911"},{"key":"ref9","first-page":"1486","article-title":"Deep generative image models using a laplacian pyramid of adversarial networks","author":"denton","year":"2015","journal-title":"Advances in neural information processing systems"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.14"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref20","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv preprint arXiv 1412 6980"},{"key":"ref45","first-page":"597","article-title":"Generative visual manipulation on the natural image manifold","author":"zhu","year":"2016","journal-title":"European Conference on Computer Vision"},{"key":"ref22","first-page":"4247","article-title":"Predicting deep zero-shot convolutional neural networks using textual descriptions","author":"lei ba","year":"2015","journal-title":"Proceedings of the IEEE International Conference on Computer Vision"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00111"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206594"},{"key":"ref42","first-page":"1425","article-title":"Label-embedding for image classification","author":"zeynep","year":"0","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"ref24","first-page":"30","article-title":"From zero-shot learning to cold-start recommendation","volume":"25","author":"li","year":"2019","journal-title":"Age"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/2964284.2964319"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00758"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00541"},{"key":"ref26","article-title":"Adversarial autoencoders","author":"makhzani","year":"2015","journal-title":"arXiv preprint arXiv 1511 05271"},{"key":"ref43","article-title":"Energy-based generative adversarial network","author":"zhao","year":"2016","journal-title":"arXiv preprint arXiv 1609 04802"},{"key":"ref25","first-page":"2579","article-title":"Visualizing data using t-sne","author":"maaten","year":"2008","journal-title":"Journal of Machine Learning Research"}],"event":{"name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","location":"Snowmass Village, CO, USA","start":{"date-parts":[[2020,3,1]]},"end":{"date-parts":[[2020,3,5]]}},"container-title":["2020 IEEE Winter Conference on Applications of Computer Vision (WACV)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9087828\/9093261\/09093610.pdf?arnumber=9093610","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T11:17:13Z","timestamp":1656587833000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9093610\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3]]},"references-count":47,"URL":"https:\/\/doi.org\/10.1109\/wacv45572.2020.9093610","relation":{},"subject":[],"published":{"date-parts":[[2020,3]]}}}