Joint Identity-aware Mixstyle and Graph-enhanced Prototype for Clothes-changing Person Re-identification

Zhiwei Zhao, Bin Liu, Yan Lu

Abstract:

In recent years, considerable progress has been
witnessed in the person re-identification (Re-ID). However, in a
more realistic long-term scenario, the appearance shift arising
from the clothes-changing inevitably deteriorates the conventional
methods that heavily depend on the clothing color. Although the
current clothes-changing person Re-ID methods introduce external
human knowledge (i.e, contour, mask) and sophisticated feature
decoupling strategy to alleviate the clothing shift, they still face
the risk of overfitting to clothing due to the limited clothing
diversity of training set. Tomore efficiently and effectively promote
the clothes-irrelevant feature learning, we present a novel joint
Identity-aware Mixstyle and Graph-enhanced Prototype method
for clothes-changing person Re-ID. Specifically, by treating the
cloth-changing as fine-grained domain/style shift, the identityaware
mixstyle (IMS) is proposed from the perspective of
domain generalization, which mixes the instance-level feature
statistics of samples within each identity to synthesize novel and
diverse clothing styles,while retaining the correspondence between
synthesized samples and latent label space. By incorporating
the IMS module, the more diverse styles can be exploited to
train a clothing-shift robust model. To further reduce the feature
discrepancy caused by clothing variations, the graph-enhanced
prototype constraint (GEP) module is proposed to explore the
graph similarity structure of style-augmented samples across
memory bank to build informative and robust prototypes, which
serve as powerful exemplars for better clothing-irrelevant metric
learning. The two modules are integrated into a joint learning
framework and benefit each other. The extensive experiments
conducted on clothes-changing person Re-ID datasets validate
the superiority and effectiveness of our method. In addition, our
method also shows good universality and corruption robustness on
other Re-ID tasks.