  Recent unsupervised person re-identification (re-ID) methods achieve high
performance by leveraging fine-grained local context. These methods are
referred to as part-based methods. However, most part-based methods obtain
local contexts through horizontal division, which suffer from misalignment due
to various human poses. Additionally, the misalignment of semantic information
in part features restricts the use of metric learning, thus affecting the
effectiveness of part-based methods. The two issues mentioned above result in
the under-utilization of part features in part-based methods. We introduce the
Spatial Cascaded Clustering and Weighted Memory (SCWM) method to address these
challenges. SCWM aims to parse and align more accurate local contexts for
different human body parts while allowing the memory module to balance hard
example mining and noise suppression. Specifically, we first analyze the
foreground omissions and spatial confusions issues in the previous method.
Then, we propose foreground and space corrections to enhance the completeness
and reasonableness of the human parsing results. Next, we introduce a weighted
memory and utilize two weighting strategies. These strategies address hard
sample mining for global features and enhance noise resistance for part
features, which enables better utilization of both global and part features.
Extensive experiments on Market-1501 and MSMT17 validate the proposed method's
effectiveness over many state-of-the-art methods.
