TY - GEN
T1 - Multi-label Fashion Image Classification with Minimal Human Supervision
AU - Inoue, Naoto
AU - Simo-Serra, Edgar
AU - Yamasaki, Toshihiko
AU - Ishikawa, Hiroshi
N1 - Funding Information:
Acknowledgements: This research was supported by GCL program of The Univ. of Tokyo by JSPS. N. Inoue and T. Yamasaki are partially supported by the Grants-in-Aid for Scientific Research (no. 26700008) from JSPS, JST-CREST (JPMJCR1686), and Microsoft IJARC core13. E. Simo-Serra and H. Ishikawa are partially supported by JST-CREST Grant Number JPMJCR14D1, JST ACT-I Grant Number JPMJPR16UD,
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - We tackle the problem of multi-label classification of fashion images, learning from noisy data with minimal human supervision. We present a new dataset of full body poses, each with a set of 66 binary labels corresponding to the information about the garments worn in the image obtained in an automatic manner. As the automatically-collected labels contain significant noise, we manually correct the labels for a small subset of the data, and use these correct labels for further training and evaluation. We build upon a recent approach that both cleans the noisy labels and learns to classify, and introduce simple changes that can significantly improve the performance.
AB - We tackle the problem of multi-label classification of fashion images, learning from noisy data with minimal human supervision. We present a new dataset of full body poses, each with a set of 66 binary labels corresponding to the information about the garments worn in the image obtained in an automatic manner. As the automatically-collected labels contain significant noise, we manually correct the labels for a small subset of the data, and use these correct labels for further training and evaluation. We build upon a recent approach that both cleans the noisy labels and learns to classify, and introduce simple changes that can significantly improve the performance.
UR - http://www.scopus.com/inward/record.url?scp=85046289547&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046289547&partnerID=8YFLogxK
U2 - 10.1109/ICCVW.2017.265
DO - 10.1109/ICCVW.2017.265
M3 - Conference contribution
AN - SCOPUS:85046289547
T3 - Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
SP - 2261
EP - 2267
BT - Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017
Y2 - 22 October 2017 through 29 October 2017
ER -