  Person re-identification (Re-ID) in real-world scenarios usually suffers from
various degradation factors, e.g., low-resolution, weak illumination, blurring
and adverse weather. On the one hand, these degradations lead to severe
discriminative information loss, which significantly obstructs identity
representation learning; on the other hand, the feature mismatch problem caused
by low-level visual variations greatly reduces retrieval performance. An
intuitive solution to this problem is to utilize low-level image restoration
methods to improve the image quality. However, existing restoration methods
cannot directly serve to real-world Re-ID due to various limitations, e.g., the
requirements of reference samples, domain gap between synthesis and reality,
and incompatibility between low-level and high-level methods. In this paper, to
solve the above problem, we propose a degradation invariance learning framework
for real-world person Re-ID. By introducing a self-supervised disentangled
representation learning strategy, our method is able to simultaneously extract
identity-related robust features and remove real-world degradations without
extra supervision. We use low-resolution images as the main demonstration, and
experiments show that our approach is able to achieve state-of-the-art
performance on several Re-ID benchmarks. In addition, our framework can be
easily extended to other real-world degradation factors, such as weak
illumination, with only a few modifications.
