Dynamic Patch-aware Enrichment Transformer for Occluded Person Re-Identification

TL;DR

This paper introduces DPEFormer, a novel transformer-based model that automatically distinguishes occlusion-free human body features for person re-identification, improving robustness without external detectors or precise alignment.

Contribution

The paper proposes a dynamic patch token selection module, a feature blending module, and a realistic occlusion augmentation strategy, advancing occluded person re-ID methods.

Findings

Significantly outperforms existing state-of-the-art methods on occluded re-ID benchmarks.

Effectively distinguishes human body features from occlusions without external detectors.

Enhances feature representation through novel modules and data augmentation strategies.

Abstract

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.