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.

本文提出了一个用于实现真实场景下的人物再识别的降解不变性学习框架，该框架采用了自我监督的分解表示学习策略，能够同时提取身份相关的强健特征，并消除真实世界中存在的降解，同时在低分辨率图像上实现了最先进的识别效果。