Relative Attribute Classification with Deep Rank SVM
Computer Vision and Pattern Recognition
2021-11-16 v1 Machine Learning
Machine Learning
Abstract
Relative attributes indicate the strength of a particular attribute between image pairs. We introduce a deep Siamese network with rank SVM loss function, called Deep Rank SVM (DRSVM), in order to decide which one of a pair of images has a stronger presence of a specific attribute. The network is trained in an end-to-end fashion to jointly learn the visual features and the ranking function. We demonstrate the effectiveness of our approach against the state-of-the-art methods on four image benchmark datasets: LFW-10, PubFig, UTZap50K-lexi and UTZap50K-2 datasets. DRSVM surpasses state-of-art in terms of the average accuracy across attributes, on three of the four image benchmark datasets.
Cite
@article{arxiv.2009.07717,
title = {Relative Attribute Classification with Deep Rank SVM},
author = {Sara Atito Ali Ahmed and Berrin Yanikoglu},
journal= {arXiv preprint arXiv:2009.07717},
year = {2021}
}