Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset
This work addresses the problem of fine-grained visual understanding in fashion for researchers and practitioners, but it is incremental as it builds upon existing instance segmentation methods.
The authors tackled the task of instance segmentation with attribute localization in fashion, introducing Fashionpedia, a dataset with 27 categories and 294 attributes, and proposed an Attribute-Mask RCNN model that improved transfer learning performance on other fashion datasets compared to ImageNet pre-training.
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 RCNN model to jointly perform instance segmentation and localized attribute recognition, and provide a novel evaluation metric for the task. We also demonstrate instance segmentation models pre-trained on Fashionpedia achieve better transfer learning performance on other fashion datasets than ImageNet pre-training. Fashionpedia is available at: https://fashionpedia.github.io/home/index.html.