  Images with different resolutions are ubiquitous in public person
re-identification (ReID) datasets and real-world scenes, it is thus crucial for
a person ReID model to handle the image resolution variations for improving its
generalization ability. However, most existing person ReID methods pay little
attention to this resolution discrepancy problem. One paradigm to deal with
this problem is to use some complicated methods for mapping all images into an
artificial image space, which however will disrupt the natural image
distribution and requires heavy image preprocessing. In this paper, we analyze
the deficiencies of several widely-used objective functions handling image
resolution discrepancies and propose a new framework called deep antithetical
learning that directly learns from the natural image space rather than creating
an arbitrary one. We first quantify and categorize original training images
according to their resolutions. Then we create an antithetical training set and
make sure that original training images have counterparts with antithetical
resolutions in this new set. At last, a novel Contrastive Center Loss(CCL) is
proposed to learn from images with different resolutions without being
interfered by their resolution discrepancies. Extensive experimental analyses
and evaluations indicate that the proposed framework, even using a vanilla deep
ReID network, exhibits remarkable performance improvements. Without bells and
whistles, our approach outperforms previous state-of-the-art methods by a large
margin.
