  Most existing Re-IDentification (Re-ID) methods are highly dependent on
precise bounding boxes that enable images to be aligned with each other.
However, due to the challenging practical scenarios, current detection models
often produce inaccurate bounding boxes, which inevitably degenerate the
performance of existing Re-ID algorithms. In this paper, we propose a novel
coarse-to-fine pyramid model to relax the need of bounding boxes, which not
only incorporates local and global information, but also integrates the gradual
cues between them. The pyramid model is able to match at different scales and
then search for the correct image of the same identity, even when the image
pairs are not aligned. In addition, in order to learn discriminative identity
representation, we explore a dynamic training scheme to seamlessly unify two
losses and extract appropriate shared information between them. Experimental
results clearly demonstrate that the proposed method achieves the
state-of-the-art results on three datasets. Especially, our approach exceeds
the current best method by 9.5% on the most challenging CUHK03 dataset.
