![Awa airline](https://loka.nahovitsyn.com/120.jpg)
![elements of style elements of style](https://cdn.shopify.com/s/files/1/0537/9391/5066/products/0699f6_5882b42f69fc4885a85d399606db9af1_mv2_900x.jpg)
Make the paragraph the unit of composition: one paragraph to each topic. DressUp! outfit synthesis through automatic optimization. Yu, L.-F., Yeung, S.-K., Terzopoulos, D., and Chan, T.Paper doll parsing: retrieving similar styles to parse clothing items. Chic or social: visual popularity analysis in online fashion networks. Runway to realway: visual analysis of fashion. Vittayakorn, S., Yamaguchi, K., Berg, A., and Berg, T.Learning visual clothing style with heterogeneous dyadic co-occurrences. Veit, A., Kovacs, B., Bell, S., McAuely, J., Bala, K., and Belongie, S.
![elements of style elements of style](http://2.bp.blogspot.com/-j-f4eLf89aw/UCUOJ0wnpbI/AAAAAAAAHeU/Ls01lF3g0Xw/s1600/Elements-of-Style-front-cover-1920.jpg)
CHIC: a combination-based recommendation system. Complete fashion coordinator: a support system for capturing and selecting daily clothes with social networks.
![elements of style elements of style](https://www.seedboxpress.com/wp-content/uploads/2020/10/Elements-of-Style-Cover-1280x2048.jpg)
Urban tribes: analyzing group photos from a social perspective. S., Bourdev, L., Kriegman, D., and Belongie, S. Remote shopping advice: enhancing in-store shopping with social technologies. Visual complexity and aesthetic perception of web pages. Michailidou, E., Harper, S., and Bechhofer, S.MALLET: a machine learning for language toolkit. Image-based recommendations on styles and substitutes. McAuley, J., Targett, C., Shi, Q., and van den Hengel, A.Foundations of statistical natural language processing. In Social Computing, Behavioral-Cultural Modeling, and Prediction. Styles in the fashion social network: an analysis on Lookbook.nu. From bikers to surfers: visual recognition of urban tribes. C., Belhumeur, P., Belongie, S., and Kriegman, D. Hipster wars: discovering elements of fashion styles. Kiapour, M., Yamaguchi, K., Berg, A., and Berg, T.What are the fashion trends in New York? In Proc. Explaining collaborative filtering recommendations. Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. The Annals of Mathematical Statistics (1961). Style Finder: fine-grained clothing style detection and retrieval. Di, W., Wah, C., Bhardwaj, A., Piramuthu, R., and Sundaresan, N.Reading tea leaves: how humans interpret topic models. Chang, J., Boyd-Graber, J., Wang, C., Gerrish, S., and Blei, D.Crowdsourcing subjective fashion advice using VizWiz: challenges and opportunities. A., Brady, E., Brewer, R., Neylan, C., Bigham, J. Automatic attribute discovery and characterization from noisy web data. CommandSpace: modeling the relationships between tasks, descriptions and features. Adar, E., Dontcheva, M., and Laput, G.We present novel, data-driven fashion applications that allow users to express their needs in natural language just as they would to a real stylist and produce tailored item recommendations for these style needs. We train the polylingual topic model (PLTM) on a set of more than half a million outfits collected from Polyvore, a popular fashion-based social net- work. Using this latent topic formation we can translate between these two languages through topic space, exposing the elements of fashion style. In this paper, we use polylingual topic modeling to learn latent fashion concepts jointly in two languages capturing these elements and styles. A dress may be "bohemian" because of its pattern, material, trim, or some combination of them: it is not always clear how low-level elements translate to high-level styles. Fashion theorists have proposed that these concepts are shaped by design elements such as color, material, and silhouette. The outfits people wear contain latent fashion concepts capturing styles, seasons, events, and environments.
![Awa airline](https://loka.nahovitsyn.com/120.jpg)