Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset

Research output: Contribution to journal › Conference article › Research › peer-review

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Fashionpedia : Ontology, Segmentation, and an Attribute Localization Dataset. / Jia, Menglin; Shi, Mengyun; Sirotenko, Mikhail; Cui, Yin; Cardie, Claire; Hariharan, Bharath; Adam, Hartwig; Belongie, Serge.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020, p. 316-332.

Research output: Contribution to journal › Conference article › Research › peer-review

Harvard

Jia, M, Shi, M, Sirotenko, M, Cui, Y, Cardie, C, Hariharan, B, Adam, H & Belongie, S 2020, 'Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 316-332. https://doi.org/10.1007/978-3-030-58452-8_19

APA

Jia, M., Shi, M., Sirotenko, M., Cui, Y., Cardie, C., Hariharan, B., Adam, H., & Belongie, S. (2020). Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 316-332. https://doi.org/10.1007/978-3-030-58452-8_19

Vancouver

Jia M, Shi M, Sirotenko M, Cui Y, Cardie C, Hariharan B et al. Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2020;316-332. https://doi.org/10.1007/978-3-030-58452-8_19

Author

Jia, Menglin ; Shi, Mengyun ; Sirotenko, Mikhail ; Cui, Yin ; Cardie, Claire ; Hariharan, Bharath ; Adam, Hartwig ; Belongie, Serge. / Fashionpedia : Ontology, Segmentation, and an Attribute Localization Dataset. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2020 ; pp. 316-332.

Bibtex

@inproceedings{32961e341f694e87bcd17d006b11fd46,
title = "Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset",
abstract = "In this work we explore the task of instance segmentation with attribute localization, which unifies instance segmentation (detect and segment each object instance) and fine-grained visual attribute categorization (recognize one or multiple attributes). The proposed task requires both localizing an object and describing its properties. To illustrate the various aspects of this task, we focus on the domain of fashion and introduce Fashionpedia as a step toward mapping out the visual aspects of the fashion world. Fashionpedia consists of two parts: (1) an ontology built by fashion experts containing 27 main apparel categories, 19 apparel parts, 294 fine-grained attributes and their relationships; (2) a dataset with everyday and celebrity event fashion images annotated with segmentation masks and their associated per-mask fine-grained attributes, built upon the Fashionpedia ontology. In order to solve this challenging task, we propose a novel Attribute-Mask R-CNN model to jointly perform instance segmentation and localized attribute recognition, and provide a novel evaluation metric for the task. Fashionpedia is available at: https://fashionpedia.github.io/home/.",
keywords = "Attribute, Dataset, Fashion, Fine-grained, Instance segmentation, Ontology",
author = "Menglin Jia and Mengyun Shi and Mikhail Sirotenko and Yin Cui and Claire Cardie and Bharath Hariharan and Hartwig Adam and Serge Belongie",
note = "Funding Information: Acknowledgements. This research was partially supported by a Google Faculty Research Award. We thank Kavita Bala, Carla Gomes, Dustin Hwang, Rohun Tripathi, Omid Poursaeed, Hector Liu, and Nayanathara Palanivel, Konstantin Lopuhin for their helpful feedback and discussion in the development of Fashionpedia dataset. We also thank Zeqi Gu, Fisher Yu, Wenqi Xian, Chao Suo, Junwen Bai, Paul Upchurch, Anmol Kabra, and Brendan Rappazzo for their help developing the fine-grained attribute annotation tool. Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 16th European Conference on Computer Vision, ECCV 2020 ; Conference date: 23-08-2020 Through 28-08-2020",
year = "2020",
doi = "10.1007/978-3-030-58452-8_19",
language = "English",
pages = "316--332",
journal = "Lecture Notes in Computer Science",
issn = "0302-9743",
publisher = "Springer Verlag",

}

RIS

TY - GEN

T1 - Fashionpedia

T2 - 16th European Conference on Computer Vision, ECCV 2020

AU - Jia, Menglin

AU - Shi, Mengyun

AU - Sirotenko, Mikhail

AU - Cui, Yin

AU - Cardie, Claire

AU - Hariharan, Bharath

AU - Adam, Hartwig

AU - Belongie, Serge

N1 - Funding Information: Acknowledgements. This research was partially supported by a Google Faculty Research Award. We thank Kavita Bala, Carla Gomes, Dustin Hwang, Rohun Tripathi, Omid Poursaeed, Hector Liu, and Nayanathara Palanivel, Konstantin Lopuhin for their helpful feedback and discussion in the development of Fashionpedia dataset. We also thank Zeqi Gu, Fisher Yu, Wenqi Xian, Chao Suo, Junwen Bai, Paul Upchurch, Anmol Kabra, and Brendan Rappazzo for their help developing the fine-grained attribute annotation tool. Publisher Copyright: © 2020, Springer Nature Switzerland AG.

PY - 2020

Y1 - 2020

N2 - In this work we explore the task of instance segmentation with attribute localization, which unifies instance segmentation (detect and segment each object instance) and fine-grained visual attribute categorization (recognize one or multiple attributes). The proposed task requires both localizing an object and describing its properties. To illustrate the various aspects of this task, we focus on the domain of fashion and introduce Fashionpedia as a step toward mapping out the visual aspects of the fashion world. Fashionpedia consists of two parts: (1) an ontology built by fashion experts containing 27 main apparel categories, 19 apparel parts, 294 fine-grained attributes and their relationships; (2) a dataset with everyday and celebrity event fashion images annotated with segmentation masks and their associated per-mask fine-grained attributes, built upon the Fashionpedia ontology. In order to solve this challenging task, we propose a novel Attribute-Mask R-CNN model to jointly perform instance segmentation and localized attribute recognition, and provide a novel evaluation metric for the task. Fashionpedia is available at: https://fashionpedia.github.io/home/.

AB - In this work we explore the task of instance segmentation with attribute localization, which unifies instance segmentation (detect and segment each object instance) and fine-grained visual attribute categorization (recognize one or multiple attributes). The proposed task requires both localizing an object and describing its properties. To illustrate the various aspects of this task, we focus on the domain of fashion and introduce Fashionpedia as a step toward mapping out the visual aspects of the fashion world. Fashionpedia consists of two parts: (1) an ontology built by fashion experts containing 27 main apparel categories, 19 apparel parts, 294 fine-grained attributes and their relationships; (2) a dataset with everyday and celebrity event fashion images annotated with segmentation masks and their associated per-mask fine-grained attributes, built upon the Fashionpedia ontology. In order to solve this challenging task, we propose a novel Attribute-Mask R-CNN model to jointly perform instance segmentation and localized attribute recognition, and provide a novel evaluation metric for the task. Fashionpedia is available at: https://fashionpedia.github.io/home/.

KW - Attribute

KW - Dataset

KW - Fashion

KW - Fine-grained

KW - Instance segmentation

KW - Ontology

UR - http://www.scopus.com/inward/record.url?scp=85097223428&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-58452-8_19

DO - 10.1007/978-3-030-58452-8_19

M3 - Conference article

AN - SCOPUS:85097223428

SP - 316

EP - 332

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

Y2 - 23 August 2020 through 28 August 2020

ER -

ID: 301822651