  Previous part-based attribute recognition approaches perform part detection
and attribute recognition in separate steps. The parts are not optimized for
attribute recognition and therefore could be sub-optimal. We present an
end-to-end deep learning approach to overcome the limitation. It generates
object parts from key points and perform attribute recognition accordingly,
allowing adaptive spatial transform of the parts. Both key point estimation and
attribute recognition are learnt jointly in a multi-task setting. Extensive
experiments on two datasets verify the efficacy of proposed end-to-end
approach.
