  Visual attributes are great means of describing images or scenes, in a way
both humans and computers understand. In order to establish a correspondence
between images and to be able to compare the strength of each property between
images, relative attributes were introduced. However, since their introduction,
hand-crafted and engineered features were used to learn increasingly complex
models for the problem of relative attributes. This limits the applicability of
those methods for more realistic cases. We introduce a deep neural network
architecture for the task of relative attribute prediction. A convolutional
neural network (ConvNet) is adopted to learn the features by including an
additional layer (ranking layer) that learns to rank the images based on these
features. We adopt an appropriate ranking loss to train the whole network in an
end-to-end fashion. Our proposed method outperforms the baseline and
state-of-the-art methods in relative attribute prediction on various coarse and
fine-grained datasets. Our qualitative results along with the visualization of
the saliency maps show that the network is able to learn effective features for
each specific attribute. Source code of the proposed method is available at
https://github.com/yassersouri/ghiaseddin.
