  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.
