  The cross-resolution person re-identification (CRReID) problem aims to match
low-resolution (LR) query identity images against high resolution (HR) gallery
images. It is a challenging and practical problem since the query images often
suffer from resolution degradation due to the different capturing conditions
from real-world cameras. To address this problem, state-of-the-art (SOTA)
solutions either learn the resolution-invariant representation or adopt
super-resolution (SR) module to recover the missing information from the LR
query. This paper explores an alternative SR-free paradigm to directly compare
HR and LR images via a dynamic metric, which is adaptive to the resolution of a
query image. We realize this idea by learning resolution-adaptive
representations for cross-resolution comparison. Specifically, we propose two
resolution-adaptive mechanisms. The first one disentangles the
resolution-specific information into different sub-vectors in the penultimate
layer of the deep neural networks, and thus creates a varying-length
representation. To better extract resolution-dependent information, we further
propose to learn resolution-adaptive masks for intermediate residual feature
blocks. A novel progressive learning strategy is proposed to train those masks
properly. These two mechanisms are combined to boost the performance of CRReID.
Experimental results show that the proposed method is superior to existing
approaches and achieves SOTA performance on multiple CRReID benchmarks.
