Optimizing domain-generalizable ReID through non-parametric normalization

Abstract

Optimizing deep neural networks to generalize effectively across diverse visual domains remains a key challenge in computer vision, especially in domain-generalizable person re-identification (ReID). The goal of domain-generalizable ReID is to develop robust deep learning (DL) models that are effective across both known (source) and unseen (target) domains. However, many top-performing ReID methods overfit to the source domain, impairing their generalization ability. Previous approaches have employed Instance Normalization (IN) with learnable parameters to generalize domains and eliminate source domain styles. Recently, some DL frameworks have adopted normalization techniques without learnable parameters. We critically examine non-parametric normalization techniques for optimizing the deep ReID model, emphasizing the advantages of using non-parametric instance normalization as a gating mechanism to extract style-independent features at various abstraction levels within both convolutional neural networks (CNNs) and Vision Transformers (ViT). Our framework offers strategic guidance on the optimal placement of non-parametric IN within the network architecture to ensure effective information flow management in subsequent layers. Additionally, we employ one-dimensional Batch Normalization (BN) without learnable parameters at deeper network levels to remove content-related biases from the source domain. Our integrated approach, termed DualNormNP, systematically optimizes the model’s capacity to generalize across varied domains. Comprehensive evaluations on multiple benchmark ReID datasets demonstrate that our approach surpasses current state-of-the-art ReID methods in terms of generalization performance. Code is available on Github: https://github.com/mdamranhossenbhuiyan/DualNormNP