Multilevel Feature Perturbation for Single-Domain Generalization Person Reidentification

Domain Generalization Person Re-identification (DG ReID) is a critical research topic for intelligent video surveillance, aiming at building a robust model generalizing to unseen domain. Particularly in single-source domain generalization (SDG) scenarios, the monotonous data style presents a significant challenge. To address this issue, existing DG methods typically employ image- or feature-level data augmentation techniques. However, these approaches may have limitations when dealing with the complexities of real-world scenarios due to their reliance on a single augmentation strategy. To overcome these limitations, we propose a novel method called Multi-Level Feature Perturbation (MLFP) for SDG ReID. MLFP integrates two complementary augmentation modules: Random Background Perturbation (RBP) at the image level and Uncertain Sampling Normalization Module (USNM) at the feature level. Specifically, RBP is designed to perform random mixture of image backgrounds at the image level, while preserving the integrity of the pedestrian subject. By focusing on the pedestrian objectives instead of the backgrounds, RBP encourages the model to learn domain-invariant, causal features and reduce the bias towards the source domain. USNM is designed to diversify the feature-level representations through manipulating feature statistics. To further enhance the learning process, we introduce a novel loss function called Improved Whitening Loss (IW Loss). This loss function is designed to capture consistent representations and explore invariant patterns among diverse feature distributions. Extensive experimental results demonstrate that our MLFP outperforms state-of-the-art methods across several SDG ReID benchmarks, highlighting its effectiveness in addressing the challenges of domain generalization.