论文标题
Fashionpedia：本体论，细分和属性本地化数据集
Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset
论文作者
论文摘要
在这项工作中，我们探索了具有属性本地化的实例分割的任务，该任务统一了实例分割（检测和细分每个对象实例）和细粒的可视属性分类（识别一个或多个属性）。所提出的任务既需要本地化对象又需要描述其属性。为了说明这项任务的各个方面，我们专注于时尚领域，并介绍Fashionpedia，以绘制时尚界的视觉方面的一步。 Fashionpedia由两个部分组成：（1）由时尚专家建立的本体，其中包含27个主要服装类别，19个服装零件，294个细粒度及其关系； （2）具有日常和名人事件的数据集，这些数据集用分割面罩注释的时尚图像及其相关的每面膜细粒属性，建立在Fashionpedia本体论的基础上。为了解决这项具有挑战性的任务，我们建议一个新颖的属性掩盖RCNN模型共同执行实例分割和局部属性识别，并为任务提供新颖的评估指标。我们还展示了对FashionPedia进行预训练的实例细分模型，在其他时尚数据集上比ImageNet预训练获得了更好的传输学习绩效。 FashionPedia可在以下网址找到：https：//fashionpedia.github.io/home/index.html。
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.
