Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset
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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
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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