Multi-Label Fashion Image Classification With Minimal Human Supervision

Naoto Inoue, Edgar Simo-Serra, Toshihiko Yamasaki, Hiroshi Ishikawa; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2261-2267

Abstract


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.

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[bibtex]
@InProceedings{Inoue_2017_ICCV,
author = {Inoue, Naoto and Simo-Serra, Edgar and Yamasaki, Toshihiko and Ishikawa, Hiroshi},
title = {Multi-Label Fashion Image Classification With Minimal Human Supervision},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2017}
}