  Cloth-changing Person Re-Identification (CC-ReID) is a challenging task that
aims to retrieve the target person across multiple surveillance cameras when
clothing changes might happen. Despite recent progress in CC-ReID, existing
approaches are still hindered by the interference of clothing variations since
they lack effective constraints to keep the model consistently focused on
clothing-irrelevant regions. To address this issue, we present a Semantic-aware
Consistency Network (SCNet) to learn identity-related semantic features by
proposing effective consistency constraints. Specifically, we generate the
black-clothing image by erasing pixels in the clothing area, which explicitly
mitigates the interference from clothing variations. In addition, to fully
exploit the fine-grained identity information, a head-enhanced attention module
is introduced, which learns soft attention maps by utilizing the proposed
part-based matching loss to highlight head information. We further design a
semantic consistency loss to facilitate the learning of high-level
identity-related semantic features, forcing the model to focus on semantically
consistent cloth-irrelevant regions. By using the consistency constraint, our
model does not require any extra auxiliary segmentation module to generate the
black-clothing image or locate the head region during the inference stage.
Extensive experiments on four cloth-changing person Re-ID datasets (LTCC, PRCC,
Vc-Clothes, and DeepChange) demonstrate that our proposed SCNet makes
significant improvements over prior state-of-the-art approaches. Our code is
available at: https://github.com/Gpn-star/SCNet.
