Style Normalization and Restitution for Generalizable Person Re-identification (2005.11037v1)

Abstract: Existing fully-supervised person re-identification (ReID) methods usually suffer from poor generalization capability caused by domain gaps. The key to solving this problem lies in filtering out identity-irrelevant interference and learning domain-invariant person representations. In this paper, we aim to design a generalizable person ReID framework which trains a model on source domains yet is able to generalize/perform well on target domains. To achieve this goal, we propose a simple yet effective Style Normalization and Restitution (SNR) module. Specifically, we filter out style variations (e.g., illumination, color contrast) by Instance Normalization (IN). However, such a process inevitably removes discriminative information. We propose to distill identity-relevant feature from the removed information and restitute it to the network to ensure high discrimination. For better disentanglement, we enforce a dual causal loss constraint in SNR to encourage the separation of identity-relevant features and identity-irrelevant features. Extensive experiments demonstrate the strong generalization capability of our framework. Our models empowered by the SNR modules significantly outperform the state-of-the-art domain generalization approaches on multiple widely-used person ReID benchmarks, and also show superiority on unsupervised domain adaptation.

Overview of Style Normalization and Restitution for Generalizable Person Re-identification

The paper addresses a critical issue in person re-identification (ReID): the challenge of generalizing model performance across different domains. Contemporary supervised ReID models often experience significant performance degradation when applied to domains not represented in the training set, a problem often attributed to domain gaps, such as variations in illumination and color contrast. To tackle this, the authors present a novel approach called Style Normalization and Restitution (SNR), which is designed to improve the generalization capability of person ReID systems without requiring access to target domain data during training.

Contribution to ReID Methodologies

The core contribution of this work is the introduction of the SNR module, which integrates style normalization through Instance Normalization (IN) and a subsequent feature restitution process. IN is used to mitigate style discrepancies by filtering out identity-irrelevant style variations. However, IN can also inadvertently remove important discriminative features, thus potentially reducing model performance. To counteract this, the SNR module restitutes these identity-relevant features by extracting them from the residual of the original and normalized information.

The authors further enhance this disentanglement process by introducing a dual causality loss constraint, which separates identity-relevant features from identity-irrelevant features, ensuring high discrimination even after style normalization. This approach significantly boosts the ReID model's generalization capability, far surpassing existing state-of-the-art domain generalization methods.

Experimental Validation

The SNR framework is evaluated on multiple ReID benchmarks, showing substantial improvements in generalization capabilities. Notably, SNR-equipped models consistently outperform previous domain generalization techniques across various datasets, including large-scale sets like Market1501 and DukeMTMC-reID, and challenging small-scale datasets such as PRID and GRID. The SNR module also enhances unsupervised domain adaptation (UDA) performance, demonstrating versatility in different ReID tasks.

Implications and Speculation on Future Developments

The paper presents significant implications for building robust ReID systems that function reliably across different domains without additional fine-tuning. This approach can potentially reduce the need for extensive data annotation and model adaptation processes typically required in practical deployment scenarios.

As a future direction, the integration of the SNR module with other backbone networks and its extension to handle cross-modality ReID tasks, such as RGB-Infrared ReID, have shown promising preliminary results. This suggests that further exploration of SNR within diverse architectures or its application to other computer vision tasks could yield broad benefits beyond the current domain generalization challenges of person ReID systems.

In conclusion, the SNR methodology advances the understanding and application of style normalization in ReID systems, paving the way for more generalizable models capable of effective performance across varied environments, a crucial step towards more resilient and adaptable AI systems in dynamic real-world settings.