A novel task of fine-grained instance segmentation with attribute localization. The proposed task unifies instance segmentation and visual attribute recognition, which is an important step toward structural understanding of visual content in real-world applications.
A unified fashion ontology informed by product descriptions from the internet and built by fashion experts. Our ontology captures the complex structure of fashion objects and ambiguity in descriptions obtained from the web, containing 46 apparel objects (27 main apparels and 19 apparel parts), and 294 fine-grained attributes (spanning 9 super categories) in total. To facilitate the development of related efforts, we also provide a mapping with categories from existing fashion segmentation datasets.
A dataset with a total of 48,825 clothing images in daily-life, street-style, celebrity events, runway, and online shopping annotated both by crowd workers for segmentation masks and fashion experts for localized attributes, with the goal of developing and benchmarking computer vision models for comprehensive understanding of fashion.
Fashionpedia dataset has 3 level of annotations:
1) Exhaustive Segmentations for main & sub-objects;
2) Fine-grained Attributes for the selected segmentation categories.;
3) Relationships & Apparel graphs (built upon on Fashionpedia Ontology).
(Read our Fashionpedia paper for more details)