  Person Re-identification (ReID) is a critical computer vision task which aims
to match the same person in images or video sequences. Most current works focus
on settings where the resolution of images is kept the same. However, the
resolution is a crucial factor in person ReID, especially when the cameras are
at different distances from the person or the camera's models are different
from each other. In this paper, we propose a novel two-stream network with a
lightweight resolution association ReID feature transformation (RAFT) module
and a self-weighted attention (SWA) ReID module to evaluate features under
different resolutions. RAFT transforms the low resolution features to
corresponding high resolution features. SWA evaluates both features to get
weight factors for the person ReID. Both modules are jointly trained to get a
resolution-invariant representation. Extensive experiments on five benchmark
datasets show the effectiveness of our method. For instance, we achieve Rank-1
accuracy of 43.3% and 83.2% on CAVIAR and MLR-CUHK03, outperforming the
state-of-the-art.
