The Fashionpedia Ontology and Fashion Segmentation Dataset

Research output: Other contribution › Research

Standard

The Fashionpedia Ontology and Fashion Segmentation Dataset. / Belongie, Serge; Jia, Menglin; Shi, Mengyun; Sirotenko, Mikhail; Cui, Yin; Hariharan, Bharath; Cardie, Claire.

5 p. 2019.

Research output: Other contribution › Research

Harvard

Belongie, S, Jia, M, Shi, M, Sirotenko, M, Cui, Y, Hariharan, B & Cardie, C 2019, The Fashionpedia Ontology and Fashion Segmentation Dataset.. <https://vision.cornell.edu/se3/wp-content/uploads/2019/06/Fashionpedia_TechReport.pdf>

APA

Belongie, S., Jia, M., Shi, M., Sirotenko, M., Cui, Y., Hariharan, B., & Cardie, C. (2019). The Fashionpedia Ontology and Fashion Segmentation Dataset. https://vision.cornell.edu/se3/wp-content/uploads/2019/06/Fashionpedia_TechReport.pdf

Vancouver

Belongie S, Jia M, Shi M, Sirotenko M, Cui Y, Hariharan B et al. The Fashionpedia Ontology and Fashion Segmentation Dataset. 2019. 5 p.

Author

Belongie, Serge ; Jia, Menglin ; Shi, Mengyun ; Sirotenko, Mikhail ; Cui, Yin ; Hariharan, Bharath ; Cardie, Claire. / The Fashionpedia Ontology and Fashion Segmentation Dataset. 2019. 5 p.

Bibtex

@misc{052b6ed0c76d42838662e5008c12c2c2,
title = "The Fashionpedia Ontology and Fashion Segmentation Dataset",
abstract = "As a step toward mapping out the visual aspects of the fashion world, we introduce the Fashionpedia ontology and fashion segmentation dataset. The Fashionpedia consists of two parts: (1) an ontology built by fashion experts containing 27 main apparel objects, 19 apparel parts, and 92 finegrained attributes and their relationships and (2) a dataset consisting of everyday and celebrity event fashion images annotated with segmentation masks and their associated fine-grained attributes, built upon the backbone of the Fashionpedia ontology structure. The aim of our work is to cultivate research connections between the computer vision and fashion communities through the creation of a high quality dataset and associated open competitions, thereby advancing the state-of-the-art in fine-grained visual recognition for fashion and apparel.",
author = "Serge Belongie and Menglin Jia and Mengyun Shi and Mikhail Sirotenko and Yin Cui and Bharath Hariharan and Claire Cardie",
year = "2019",
language = "English",
type = "Other",

}

RIS

TY - GEN

T1 - The Fashionpedia Ontology and Fashion Segmentation Dataset

AU - Belongie, Serge

AU - Jia, Menglin

AU - Shi, Mengyun

AU - Sirotenko, Mikhail

AU - Cui, Yin

AU - Hariharan, Bharath

AU - Cardie, Claire

PY - 2019

Y1 - 2019

N2 - As a step toward mapping out the visual aspects of the fashion world, we introduce the Fashionpedia ontology and fashion segmentation dataset. The Fashionpedia consists of two parts: (1) an ontology built by fashion experts containing 27 main apparel objects, 19 apparel parts, and 92 finegrained attributes and their relationships and (2) a dataset consisting of everyday and celebrity event fashion images annotated with segmentation masks and their associated fine-grained attributes, built upon the backbone of the Fashionpedia ontology structure. The aim of our work is to cultivate research connections between the computer vision and fashion communities through the creation of a high quality dataset and associated open competitions, thereby advancing the state-of-the-art in fine-grained visual recognition for fashion and apparel.

AB - As a step toward mapping out the visual aspects of the fashion world, we introduce the Fashionpedia ontology and fashion segmentation dataset. The Fashionpedia consists of two parts: (1) an ontology built by fashion experts containing 27 main apparel objects, 19 apparel parts, and 92 finegrained attributes and their relationships and (2) a dataset consisting of everyday and celebrity event fashion images annotated with segmentation masks and their associated fine-grained attributes, built upon the backbone of the Fashionpedia ontology structure. The aim of our work is to cultivate research connections between the computer vision and fashion communities through the creation of a high quality dataset and associated open competitions, thereby advancing the state-of-the-art in fine-grained visual recognition for fashion and apparel.

M3 - Other contribution

ER -

ID: 306896814