  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.
