  Person re-identification (re-ID) continues to pose a significant challenge,
particularly in scenarios involving occlusions. Prior approaches aimed at
tackling occlusions have predominantly focused on aligning physical body
features through the utilization of external semantic cues. However, these
methods tend to be intricate and susceptible to noise. To address the
aforementioned challenges, we present an innovative end-to-end solution known
as the Dynamic Patch-aware Enrichment Transformer (DPEFormer). This model
effectively distinguishes human body information from occlusions automatically
and dynamically, eliminating the need for external detectors or precise image
alignment. Specifically, we introduce a dynamic patch token selection module
(DPSM). DPSM utilizes a label-guided proxy token as an intermediary to identify
informative occlusion-free tokens. These tokens are then selected for deriving
subsequent local part features. To facilitate the seamless integration of
global classification features with the finely detailed local features selected
by DPSM, we introduce a novel feature blending module (FBM). FBM enhances
feature representation through the complementary nature of information and the
exploitation of part diversity. Furthermore, to ensure that DPSM and the entire
DPEFormer can effectively learn with only identity labels, we also propose a
Realistic Occlusion Augmentation (ROA) strategy. This strategy leverages the
recent advances in the Segment Anything Model (SAM). As a result, it generates
occlusion images that closely resemble real-world occlusions, greatly enhancing
the subsequent contrastive learning process. Experiments on occluded and
holistic re-ID benchmarks signify a substantial advancement of DPEFormer over
existing state-of-the-art approaches. The code will be made publicly available.
