  Relative attribute (RA), referring to the preference over two images on the
strength of a specific attribute, can enable fine-grained image-to-image
translation due to its rich semantic information. Existing work based on RAs
however failed to reconcile the goal for fine-grained translation and the goal
for high-quality generation. We propose a new model TRIP to coordinate these
two goals for high-quality fine-grained translation. In particular, we
simultaneously train two modules: a generator that translates an input image to
the desired image with smooth subtle changes with respect to the interested
attributes; and a ranker that ranks rival preferences consisting of the input
image and the desired image. Rival preferences refer to the adversarial ranking
process: (1) the ranker thinks no difference between the desired image and the
input image in terms of the desired attributes; (2) the generator fools the
ranker to believe that the desired image changes the attributes over the input
image as desired. RAs over pairs of real images are introduced to guide the
ranker to rank image pairs regarding the interested attributes only. With an
effective ranker, the generator would "win" the adversarial game by producing
high-quality images that present desired changes over the attributes compared
to the input image. The experiments on two face image datasets and one shoe
image dataset demonstrate that our TRIP achieves state-of-art results in
generating high-fidelity images which exhibit smooth changes over the
interested attributes.
