TY - GEN
T1 - What Makes a Style
T2 - 16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017
AU - Takagi, Moeko
AU - Simo-Serra, Edgar
AU - Iizuka, Satoshi
AU - Ishikawa, Hiroshi
N1 - Funding Information:
Acknowledgements: This work was partially supported by JST-CREST Grant Number JPMJCR14D1.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - In this work, we perform an experimental analysis of the differences of both how humans and machines see and distinguish fashion styles. For this purpose, we propose an expert-curated new dataset for fashion style prediction, which consists of 14 different fashion styles each with roughly 1,000 images of worn outfits. The dataset, with a total of 13,126 images, captures the diversity and complexity of modern fashion styles. We perform an extensive analysis of the dataset by benchmarking a wide variety of modern classification networks, and also perform an in-depth user study with both fashion-savvy and fashion-naïve users. Our results indicate that, although classification networks are able to outperform naive users, they are still far from the performance of savvy users, for which it is important to not only consider texture and color, but subtle differences in the combination of garments.
AB - In this work, we perform an experimental analysis of the differences of both how humans and machines see and distinguish fashion styles. For this purpose, we propose an expert-curated new dataset for fashion style prediction, which consists of 14 different fashion styles each with roughly 1,000 images of worn outfits. The dataset, with a total of 13,126 images, captures the diversity and complexity of modern fashion styles. We perform an extensive analysis of the dataset by benchmarking a wide variety of modern classification networks, and also perform an in-depth user study with both fashion-savvy and fashion-naïve users. Our results indicate that, although classification networks are able to outperform naive users, they are still far from the performance of savvy users, for which it is important to not only consider texture and color, but subtle differences in the combination of garments.
UR - http://www.scopus.com/inward/record.url?scp=85046291667&partnerID=8YFLogxK
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U2 - 10.1109/ICCVW.2017.263
DO - 10.1109/ICCVW.2017.263
M3 - Conference contribution
AN - SCOPUS:85046291667
T3 - Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
SP - 2247
EP - 2253
BT - Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 22 October 2017 through 29 October 2017
ER -