  Person re-identification (re-ID) solves the task of matching images across
cameras and is among the research topics in vision community. Since query
images in real-world scenarios might suffer from resolution loss, how to solve
the resolution mismatch problem during person re-ID becomes a practical
problem. Instead of applying separate image super-resolution models, we propose
a novel network architecture of Resolution Adaptation and re-Identification
Network (RAIN) to solve cross-resolution person re-ID. Advancing the strategy
of adversarial learning, we aim at extracting resolution-invariant
representations for re-ID, while the proposed model is learned in an end-to-end
training fashion. Our experiments confirm that the use of our model can
recognize low-resolution query images, even if the resolution is not seen
during training. Moreover, the extension of our model for semi-supervised re-ID
further confirms the scalability of our proposed method for real-world
scenarios and applications.
