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Paper Doll
2D Classification
许可协议: Unknown

Overview

Clothing recognition is an extremely challenging problem due to wide variation in clothing item appearance, layering, and style. In this paper, we tackle the clothing parsing problem using a retrieval based approach. For a query image, we find similar styles from a large database of tagged fashion images and use these examples to effectively parse the query. Our approach combines: trained global models of clothing items, local clothing models learned on the fly from retrieved examples, and mask transfer (paper doll item transfer) from retrieved examples to the query. Experimental evaluation shows that our approach significantly outperforms state of the art in parsing accuracy.

数据概要
数据格式
image,
数据量
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文件大小
--
发布方
Kota Yamaguchi,M. Hadi Kiapour,Tamara L. Berg
| 数据量 -- | 大小 --
Paper Doll
2D Classification
许可协议: Unknown

Overview

Clothing recognition is an extremely challenging problem due to wide variation in clothing item appearance, layering, and style. In this paper, we tackle the clothing parsing problem using a retrieval based approach. For a query image, we find similar styles from a large database of tagged fashion images and use these examples to effectively parse the query. Our approach combines: trained global models of clothing items, local clothing models learned on the fly from retrieved examples, and mask transfer (paper doll item transfer) from retrieved examples to the query. Experimental evaluation shows that our approach significantly outperforms state of the art in parsing accuracy.

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