TY - GEN
T1 - Learning Type-Aware Embeddings for Fashion Compatibility
AU - Vasileva, Mariya I.
AU - Plummer, Bryan A.
AU - Dusad, Krishna
AU - Rajpal, Shreya
AU - Kumar, Ranjitha
AU - Forsyth, David
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - Outfits in online fashion data are composed of items of many different types (e.g. top, bottom, shoes) that share some stylistic relationship with one another. A representation for building outfits requires a method that can learn both notions of similarity (for example, when two tops are interchangeable) and compatibility (items of possibly different type that can go together in an outfit). This paper presents an approach to learning an image embedding that respects item type, and jointly learns notions of item similarity and compatibility in an end-to-end model. To evaluate the learned representation, we crawled 68,306 outfits created by users on the Polyvore website. Our approach obtains 3–5% improvement over the state-of-the-art on outfit compatibility prediction and fill-in-the-blank tasks using our dataset, as well as an established smaller dataset, while supporting a variety of useful queries (Code and data: https://github.com/mvasil/fashion-compatibility ).
AB - Outfits in online fashion data are composed of items of many different types (e.g. top, bottom, shoes) that share some stylistic relationship with one another. A representation for building outfits requires a method that can learn both notions of similarity (for example, when two tops are interchangeable) and compatibility (items of possibly different type that can go together in an outfit). This paper presents an approach to learning an image embedding that respects item type, and jointly learns notions of item similarity and compatibility in an end-to-end model. To evaluate the learned representation, we crawled 68,306 outfits created by users on the Polyvore website. Our approach obtains 3–5% improvement over the state-of-the-art on outfit compatibility prediction and fill-in-the-blank tasks using our dataset, as well as an established smaller dataset, while supporting a variety of useful queries (Code and data: https://github.com/mvasil/fashion-compatibility ).
KW - Appearance representations
KW - Embedding methods
KW - Fashion
UR - http://www.scopus.com/inward/record.url?scp=85055095935&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85055095935&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-01270-0_24
DO - 10.1007/978-3-030-01270-0_24
M3 - Conference contribution
AN - SCOPUS:85055095935
SN - 9783030012694
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 405
EP - 421
BT - Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings
A2 - Weiss, Yair
A2 - Ferrari, Vittorio
A2 - Sminchisescu, Cristian
A2 - Hebert, Martial
PB - Springer
T2 - 15th European Conference on Computer Vision, ECCV 2018
Y2 - 8 September 2018 through 14 September 2018
ER -