FashionAI: A Hierarchical Dataset for Fashion Understanding

Xingxing Zou, Xiangheng Kong, Waikeung Wong, Congde Wang, Yuguang Liu, Yang Cao; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

Abstract


Fine-grained attribute recognition is critical for fashion understanding, yet is missing in existing professional and comprehensive fashion datasets. In this paper, we present a large scale attribute dataset with manual annotation in high quality. To this end, complex fashion knowledge is disassembled into mutually exclusive concepts and form a hierarchical structure to describe the cognitive process. Such well-structured knowledge is reflected by dataset in terms of its clear definition and precise annotation. The problems which are common in the process of annotation, including structured noise, occlusion, uncertain problems, and attribute inconsistency, are well addressed instead of merely discarding those bad data. Further, we propose an iterative process of building a dataset with practical usefulness. With 24 key points, 245 labels that cover 6 categories of women's clothing, and a total of 41 subcategories, the cre- ation of our dataset drew upon a large amount of crowd staff engagement. Extensive experiments quantitatively and qualitatively demonstrate its effectiveness.

Related Material


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[bibtex]
@InProceedings{Zou_2019_CVPR_Workshops,
author = {Zou, Xingxing and Kong, Xiangheng and Wong, Waikeung and Wang, Congde and Liu, Yuguang and Cao, Yang},
title = {FashionAI: A Hierarchical Dataset for Fashion Understanding},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}