  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.
