Proceedings ArticleDOI
Paper Doll Parsing: Retrieving Similar Styles to Parse Clothing Items
Kota Yamaguchi,M. Hadi Kiapour,Tamara L. Berg +2 more
- pp 3519-3526
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TLDR
This paper tackles the clothing parsing problem using a retrieval based approach that combines parsing from: pre-trained global clothing models, local clothing models learned on the fly from retrieved examples, and transferred parse masks (paper doll item transfer) from retrieved example.Abstract:
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 parse the query. Our approach combines parsing from: pre-trained global clothing models, local clothing models learned on the fly from retrieved examples, and transferred parse masks (paper doll item transfer) from retrieved examples. Experimental evaluation shows that our approach significantly outperforms state of the art in parsing accuracy.read more
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Image-Based Recommendations on Styles and Substitutes
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DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations
TL;DR: This work introduces DeepFashion1, a large-scale clothes dataset with comprehensive annotations, and proposes a new deep model, namely FashionNet, which learns clothing features by jointly predicting clothing attributes and landmarks.
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Look into Person: Self-Supervised Structure-Sensitive Learning and a New Benchmark for Human Parsing
TL;DR: A new benchmark Look into Person (LIP) is introduced that makes a significant advance in terms of scalability, diversity and difficulty, and a novel self-supervised structure-sensitive learning approach, which imposes human pose structures into parsing results without resorting to extra supervision.
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