TY - GEN
T1 - Multi-modal Embedding for Main Product Detection in Fashion
AU - Yu, Long Long
AU - Simo-Serra, Edgar
AU - Moreno-Noguer, Francesc
AU - Rubio, Antonio
N1 - Funding Information:
Acknowledgments: This work is partly funded by the Spanish MINECO project RobInstruct TIN2014-58178-R. A.Rubio is supported by the industrial doctorate grant 2015-DI-010 of the AGAUR. The authors are grateful to the NVIDIA donation program for its support with GPU cards.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - We 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.
AB - We 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.
UR - http://www.scopus.com/inward/record.url?scp=85046277673&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046277673&partnerID=8YFLogxK
U2 - 10.1109/ICCVW.2017.261
DO - 10.1109/ICCVW.2017.261
M3 - Conference contribution
AN - SCOPUS:85046277673
T3 - Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
SP - 2236
EP - 2242
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 -