End to End Localization and Ranking for Relative Attributes
We propose an end to end deep convolutional network to simultaneously localize and rank relative visual attributes, given only weakly supervised pairwise image comparisons. Unlike previous methods, our network jointly learns the attribute's features, localization, and ranker. The localization module of our network discovers the most informative image region for the attribute, which is then used by the ranking module to learn a ranking model of the attribute. Our end to end framework also significantly speeds up processing and is much faster than previous methods. We show state of the art ranking results on various relative attribute datasets, and our qualitative localization results clearly demonstrate our network's ability to learn meaningful image patches.
ECCV