-
Multi-Modal Embedding for Main Product Detection in Fashion
AbstractWe present an approach to detect the main product in fashion images by exploiting the textual metadata associated with each image. Our approach is based on a Convolutional Neural Network and learns a joint embedding of object proposals and textual metadata to predict the main product in the image. We additionally use several complementary classification and overlap losses in order to improve training stability and performance. Our tests on a large-scale dataset taken from eight e-commerce sites show that our approach outperforms strong baselines and is able to accurately detect the main product in a wide diversity of challenging fashion images.
Related Material
[pdf][bibtex]@InProceedings{Rubio_2017_ICCV,
author = {Rubio, Antonio and Yu, LongLong and Simo-Serra, Edgar and Moreno-Noguer, Francesc},
title = {Multi-Modal Embedding for Main Product Detection in Fashion},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2017}
}
These ICCV 2017 papers are the Open Access versions, provided by the Computer Vision Foundation.
Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore.
Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore.
This material is presented to ensure timely dissemination of scholarly and technical work.
Copyright and all rights therein are retained by authors or by other copyright holders.
All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright.