  Existing person re-identification (re-id) methods either assume the
availability of well-aligned person bounding box images as model input or rely
on constrained attention selection mechanisms to calibrate misaligned images.
They are therefore sub-optimal for re-id matching in arbitrarily aligned person
images potentially with large human pose variations and unconstrained
auto-detection errors. In this work, we show the advantages of jointly learning
attention selection and feature representation in a Convolutional Neural
Network (CNN) by maximising the complementary information of different levels
of visual attention subject to re-id discriminative learning constraints.
Specifically, we formulate a novel Harmonious Attention CNN (HA-CNN) model for
joint learning of soft pixel attention and hard regional attention along with
simultaneous optimisation of feature representations, dedicated to optimise
person re-id in uncontrolled (misaligned) images. Extensive comparative
evaluations validate the superiority of this new HA-CNN model for person re-id
over a wide variety of state-of-the-art methods on three large-scale benchmarks
including CUHK03, Market-1501, and DukeMTMC-ReID.
