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Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12346)

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

Dataset Ontology Instance segmentation Fine-grained Attribute Fashion 

Notes

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.

Supplementary material

500725_1_En_19_MOESM1_ESM.pdf (4.8 mb)
Supplementary material 1 (pdf 4942 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Cornell UniversityIthacaUSA
  2. 2.Cornell TechNew YorkUSA
  3. 3.Google ResearchNew YorkUSA
  4. 4.Hearst MagazinesNew YorkUSA

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