{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T18:45:11Z","timestamp":1768589111510,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":40,"publisher":"ACM","license":[{"start":{"date-parts":[[2018,6,5]],"date-time":"2018-06-05T00:00:00Z","timestamp":1528156800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Key Research and Development Program","award":["2016YFB1200203"],"award-info":[{"award-number":["2016YFB1200203"]}]},{"name":"Key Research and Development Program of Zhejiang Province","award":["2018C01004"],"award-info":[{"award-number":["2018C01004"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2017FZA5014"],"award-info":[{"award-number":["2017FZA5014"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61572428,U1509206"],"award-info":[{"award-number":["61572428,U1509206"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2018,6,5]]},"DOI":"10.1145\/3206025.3206048","type":"proceedings-article","created":{"date-parts":[[2018,6,11]],"date-time":"2018-06-11T12:36:20Z","timestamp":1528720580000},"page":"143-151","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["Interpretable Partitioned Embedding for Customized Multi-item Fashion Outfit Composition"],"prefix":"10.1145","author":[{"given":"Zunlei","family":"Feng","sequence":"first","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"given":"Zhenyun","family":"Yu","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"given":"Yezhou","family":"Yang","sequence":"additional","affiliation":[{"name":"Arizona State University, Phoenix, AZ, USA"}]},{"given":"Yongcheng","family":"Jing","sequence":"additional","affiliation":[{"name":"Zhejiang University, HangZhou, China"}]},{"given":"Junxiao","family":"Jiang","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}]},{"given":"Mingli","family":"Song","sequence":"additional","affiliation":[{"name":"Zhejiang University, HangZhou, China"}]}],"member":"320","published-online":{"date-parts":[[2018,6,5]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Changing Fashion Cultures. arXiv preprint arXiv:1703.07920","author":"Abe Kaori","year":"2017","unstructured":"Kaori Abe , Teppei Suzuki , Shunya Ueta , Akio Nakamura , Yutaka Satoh , and Hirokatsu Kataoka . 2017. Changing Fashion Cultures. arXiv preprint arXiv:1703.07920 ( 2017 ). Kaori Abe, Teppei Suzuki, Shunya Ueta, Akio Nakamura, Yutaka Satoh, and Hirokatsu Kataoka. 2017. Changing Fashion Cultures. arXiv preprint arXiv:1703.07920 (2017)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-37447-0_25"},{"key":"e_1_3_2_1_3_1","volume-title":"Multi-level variational autoencoder: Learning disentangled representations from grouped observations. arXiv preprint arX-iv:1705.08841","author":"Bouchacourt Diane","year":"2017","unstructured":"Diane Bouchacourt , Ryota Tomioka , and Sebastian Nowozin . 2017. Multi-level variational autoencoder: Learning disentangled representations from grouped observations. arXiv preprint arX-iv:1705.08841 ( 2017 ). Diane Bouchacourt, Ryota Tomioka, and Sebastian Nowozin. 2017. Multi-level variational autoencoder: Learning disentangled representations from grouped observations. arXiv preprint arX-iv:1705.08841 (2017)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33712-3_44"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299169"},{"key":"e_1_3_2_1_6_1","volume-title":"Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In Advances in Neural Information Processing Systems. 2172--2180.","author":"Chen Xi","year":"2016","unstructured":"Xi Chen , Yan Duan , Rein Houthooft , John Schulman , Ilya Sutskever , and Pieter Abbeel . 2016 . Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In Advances in Neural Information Processing Systems. 2172--2180. Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. 2016. Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In Advances in Neural Information Processing Systems. 2172--2180."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.07.043"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-37444-9_33"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.382"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2872427.2883037"},{"key":"e_1_3_2_1_11_1","unstructured":"Irina Higgins Loic Matthey Arka Pal Christopher Burgess Xavier Glorot Matthew Botvinick Shakir Mohamed and Alexan- der Lerchner. 2016. beta-vae: Learning basic visual concepts with a constrained variational framework. (2016).  Irina Higgins Loic Matthey Arka Pal Christopher Burgess Xavier Glorot Matthew Botvinick Shakir Mohamed and Alexan- der Lerchner. 2016. beta-vae: Learning basic visual concepts with a constrained variational framework. (2016)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.127"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.5555\/2283696.2283775"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623332"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10590-1_31"},{"key":"e_1_3_2_1_16_1","volume-title":"Auto-Encoding Variational Bayes. stat 1050","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Max Welling . 2014. Auto-Encoding Variational Bayes. stat 1050 ( 2014 ), 1. Diederik P Kingma and Max Welling. 2014. Auto-Encoding Variational Bayes. stat 1050 (2014), 1."},{"key":"e_1_3_2_1_17_1","unstructured":"Tejas D Kulkarni William F Whitney Pushmeet Kohli and Josh Tenenbaum. 2015. Deep convolutional inverse graphics network. In Advances in Neural Information Processing Systems. 2539--2547.   Tejas D Kulkarni William F Whitney Pushmeet Kohli and Josh Tenenbaum. 2015. Deep convolutional inverse graphics network. In Advances in Neural Information Processing Systems. 2539--2547."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2017.2690144"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080658"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.124"},{"key":"e_1_3_2_1_21_1","first-page":"2579","article-title":"Visualizing data using t-SNE","author":"van der Maaten Laurens","year":"2008","unstructured":"Laurens van der Maaten and Geoffrey Hinton . 2008 . Visualizing data using t-SNE . Journal of machine learning research 9 , Nov (2008), 2579 -- 2605 . Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research 9, Nov (2008), 2579--2605.","journal-title":"Journal of machine learning research 9"},{"key":"e_1_3_2_1_22_1","volume-title":"Street- Style: Exploring world-wide clothing styles from millions of photos. arXiv preprint arXiv:1706.01869","author":"Matzen Kevin","year":"2017","unstructured":"Kevin Matzen , Kavita Bala , and Noah Snavely . 2017 . Street- Style: Exploring world-wide clothing styles from millions of photos. arXiv preprint arXiv:1706.01869 (2017). Kevin Matzen, Kavita Bala, and Noah Snavely. 2017. Street- Style: Exploring world-wide clothing styles from millions of photos. arXiv preprint arXiv:1706.01869 (2017)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766462.2767755"},{"key":"e_1_3_2_1_24_1","unstructured":"Jan Morovic. 1998. To develop a universal gamut mapping algorithm. (1998).  Jan Morovic. 1998. To develop a universal gamut mapping algorithm. (1998)."},{"key":"e_1_3_2_1_25_1","volume-title":"Modeling visual com- patibility through hierarchical mid-level elements. arXiv preprint arXiv:1604.00036","author":"Oramas Jose","year":"2016","unstructured":"Jose Oramas and Tinne Tuytelaars . 2016. Modeling visual com- patibility through hierarchical mid-level elements. arXiv preprint arXiv:1604.00036 ( 2016 ). Jose Oramas and Tinne Tuytelaars. 2016. Modeling visual com- patibility through hierarchical mid-level elements. arXiv preprint arXiv:1604.00036 (2016)."},{"key":"e_1_3_2_1_26_1","volume-title":"Invertible conditional gans for image editing. arXiv preprint arXiv:1611.06355","author":"Perarnau Guim","year":"2016","unstructured":"Guim Perarnau , Joost van de Weijer , Bogdan Raducanu , and Jose M \u00c1lvarez . 2016. Invertible conditional gans for image editing. arXiv preprint arXiv:1611.06355 ( 2016 ). Guim Perarnau, Joost van de Weijer, Bogdan Raducanu, and Jose M \u00c1lvarez. 2016. Invertible conditional gans for image editing. arXiv preprint arXiv:1611.06355 (2016)."},{"key":"e_1_3_2_1_27_1","volume-title":"Learning factorial codes by predictability minimization. Learning 4, 6","author":"Schmidhuber J\u00fcrgen","year":"2008","unstructured":"J\u00fcrgen Schmidhuber . 2008. Learning factorial codes by predictability minimization. Learning 4, 6 ( 2008 ). J\u00fcrgen Schmidhuber. 2008. Learning factorial codes by predictability minimization. Learning 4, 6 (2008)."},{"key":"e_1_3_2_1_28_1","unstructured":"N Siddharth Brooks Paige Alban Desmaison Jan-Willem van de Meent Frank Wood Noah D Goodman Pushmeet Kohli and Philip HS Torr. 2016. Learning Disentangled Representations in Deep Generative Models. (2016).  N Siddharth Brooks Paige Alban Desmaison Jan-Willem van de Meent Frank Wood Noah D Goodman Pushmeet Kohli and Philip HS Torr. 2016. Learning Disentangled Representations in Deep Generative Models. (2016)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298688"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.39"},{"key":"e_1_3_2_1_31_1","volume-title":"A law of comparative judgment. Psychological review 34, 4","author":"Thurstone Louis L","year":"1927","unstructured":"Louis L Thurstone . 1927. A law of comparative judgment. Psychological review 34, 4 ( 1927 ), 273. Louis L Thurstone. 1927. A law of comparative judgment. Psychological review 34, 4 (1927), 273."},{"key":"e_1_3_2_1_32_1","volume-title":"Conditional similarity networks. Computer Vision and Pattern Recognition (CVPR 2017)","author":"Veit Andreas","year":"2017","unstructured":"Andreas Veit , Serge Belongie , and Theofanis Karaletsos . 2017. Conditional similarity networks. Computer Vision and Pattern Recognition (CVPR 2017) ( 2017 ). Andreas Veit, Serge Belongie, and Theofanis Karaletsos. 2017. Conditional similarity networks. Computer Vision and Pattern Recognition (CVPR 2017) (2017)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.527"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2015.131"},{"key":"e_1_3_2_1_35_1","volume-title":"Proceedings of the Twenty-Sixth International Joint Conference on Arti cial Intelligence, IJCAI. 2901--2907","author":"Wang Chaoyue","year":"2017","unstructured":"Chaoyue Wang , Chaohui Wang , Chang Xu , and Dacheng Tao . 2017 . Tag disentangled generative adversarial network for ob- ject image re-rendering . In Proceedings of the Twenty-Sixth International Joint Conference on Arti cial Intelligence, IJCAI. 2901--2907 . Chaoyue Wang, Chaohui Wang, Chang Xu, and Dacheng Tao. 2017. Tag disentangled generative adversarial network for ob- ject image re-rendering. In Proceedings of the Twenty-Sixth International Joint Conference on Arti cial Intelligence, IJCAI. 2901--2907."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_20"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/2072298.2072013"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.5555\/2354409.2355126"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"crossref","unstructured":"Kota Yamaguchi Takayuki Okatani Kyoko Sudo Kazuhiko Murasaki and Yukinobu Taniguchi. 2015. Mix and Match: Joint Model for Clothing and Attribute Recognition.. In BMVC. 51--1.  Kota Yamaguchi Takayuki Okatani Kyoko Sudo Kazuhiko Murasaki and Yukinobu Taniguchi. 2015. Mix and Match: Joint Model for Clothing and Attribute Recognition.. In BMVC. 51--1.","DOI":"10.5244\/C.29.51"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/2964284.2973834"}],"event":{"name":"ICMR '18: International Conference on Multimedia Retrieval","location":"Yokohama Japan","acronym":"ICMR '18","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3206025.3206048","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3206025.3206048","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T01:08:15Z","timestamp":1750208895000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3206025.3206048"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,5]]},"references-count":40,"alternative-id":["10.1145\/3206025.3206048","10.1145\/3206025"],"URL":"https:\/\/doi.org\/10.1145\/3206025.3206048","relation":{},"subject":[],"published":{"date-parts":[[2018,6,5]]},"assertion":[{"value":"2018-06-05","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}