Person images captured by public surveillance cameras often have low resolutions (LRs), along with uncontrolled pose variations, background clutter and occlusion. These issues cause the resolution mismatch problem when matched with high-resolution (HR) gallery images (typically available during collection), harming the person re-identification (re-id) performance. While a number of methods have been introduced based on the joint learning of super-resolution and person re-id, they ignore specific discriminant identity information encoded in LR person images, leading to ineffective model performance. In this work, we propose a novel joint bilateral-resolution identity modeling method that concurrently performs HR-specific identity feature learning with super-resolution, LR-specific identity feature learning, and person re-id optimization. We also introduce an adaptive ensemble algorithm for handling different low resolutions. Extensive evaluations validate the advantages of our method over related state-of-the-art re-id and super-resolution methods on cross-resolution re-id benchmarks. An important discovery is that leveraging LR-specific identity information enables a simple cascade of super-resolution and person re-id learning to achieve state-of-the-art performance, without elaborate model design nor bells and whistles, which has not been investigated before.

公共监控摄像机捕获的人物图像通常具有低分辨率 (LR)，以及不受控制的姿势变化、背景杂乱和遮挡。这些问题导致分辨率不匹配与高分辨率 (HR) 画廊图像（通常在收集期间可用）匹配时出现问题，从而损害人员重新识别 (re-id) 性能。虽然已经引入了许多基于超分辨率和行人重识别联合学习的方法，但它们忽略了 LR 行人图像中编码的特定判别身份信息，导致模型性能低下。在这项工作中，我们提出了一种新的联合双边分辨率身份建模方法，该方法同时执行 HR 特定的身份特征学习与超分辨率、LR 特定的身份特征学习和行人重新识别优化。我们还介绍了一种用于处理不同低分辨率的自适应集成算法。广泛的评估验证了我们的方法在交叉分辨率重新识别基准上相对于相关的最先进重新识别和超分辨率方法的优势。一个重要的发现是，利用 LR 特定的身份信息，可以通过简单的超分辨率和行人重新识别学习级联来实现最先进的性能，而无需精心设计的模型设计，也没有花里胡哨的东西，这一点尚未得到研究前。