With the rapid advancements in deep learning technologies, person re-identification (ReID) has witnessed remarkable performance improvements. However, the majority of prior works have traditionally focused on solving the problem via extracting features solely from a single perspective, such as uniform partitioning, hard attention mechanisms, or semantic masks. While these approaches have demonstrated efficacy within specific contexts, they fall short in diverse situations. In this paper, we propose a novel approach, Mutual Distillation Learning For Person Re-identification (termed as MDPR), which addresses the challenging problem from multiple perspectives within a single unified model, leveraging the power of mutual distillation to enhance the feature representations collectively. Specifically, our approach encompasses two branches: a hard content branch to extract local features via a uniform horizontal partitioning strategy and a Soft Content Branch to dynamically distinguish between foreground and background and facilitate the extraction of multi-granularity features via a carefully designed attention mechanism. To facilitate knowledge exchange between these two branches, a mutual distillation and fusion process is employed, promoting the capability of the outputs of each branch. Extensive experiments are conducted on widely used person ReID datasets to validate the effectiveness and superiority of our approach. Notably, our method achieves an impressive $88.7\%/94.4\%$ in mAP/Rank-1 on the DukeMTMC-reID dataset, surpassing the current state-of-the-art results. Our source code is available at https://github.com/KuilongCui/MDPR.

翻译：随着深度学习技术的快速发展，行人重识别（ReID）在性能上取得了显著提升。然而，以往大多数研究通常仅从单一视角（如均匀划分、硬注意力机制或语义掩码）提取特征以解决该问题。尽管这些方法在特定情境下展现了有效性，但在多样化场景中仍存在不足。本文提出了一种新颖方法——面向行人重识别的互蒸馏学习（称为MDPR），该方法在单个统一模型内从多视角处理这一挑战性问题，借助互蒸馏技术协同增强特征表示。具体而言，我们的方法包含两个分支：硬内容分支通过均匀水平划分策略提取局部特征，以及软内容分支通过精心设计的注意力机制动态区分前景与背景，促进多粒度特征的提取。为促进两分支间的知识交换，我们采用了互蒸馏与融合过程，增强了各分支输出的能力。在广泛使用的行人重识别数据集上进行了大量实验，验证了我们方法的有效性和优越性。值得注意的是，在DukeMTMC-reID数据集上，我们的方法在mAP/Rank-1指标上分别达到了88.7%/94.4%的出色结果，超越了当前最先进水平。我们的源代码已开源至https://github.com/KuilongCui/MDPR。