Debiased Batch Normalization via Gaussian Process for Generalizable Person Re-Identification

by   Jiawei Liu, et al.

Generalizable person re-identification aims to learn a model with only several labeled source domains that can perform well on unseen domains. Without access to the unseen domain, the feature statistics of the batch normalization (BN) layer learned from a limited number of source domains is doubtlessly biased for unseen domain. This would mislead the feature representation learning for unseen domain and deteriorate the generalizaiton ability of the model. In this paper, we propose a novel Debiased Batch Normalization via Gaussian Process approach (GDNorm) for generalizable person re-identification, which models the feature statistic estimation from BN layers as a dynamically self-refining Gaussian process to alleviate the bias to unseen domain for improving the generalization. Specifically, we establish a lightweight model with multiple set of domain-specific BN layers to capture the discriminability of individual source domain, and learn the corresponding parameters of the domain-specific BN layers. These parameters of different source domains are employed to deduce a Gaussian process. We randomly sample several paths from this Gaussian process served as the BN estimations of potential new domains outside of existing source domains, which can further optimize these learned parameters from source domains, and estimate more accurate Gaussian process by them in return, tending to real data distribution. Even without a large number of source domains, GDNorm can still provide debiased BN estimation by using the mean path of the Gaussian process, while maintaining low computational cost during testing. Extensive experiments demonstrate that our GDNorm effectively improves the generalization ability of the model on unseen domain.


TAL: Two-stream Adaptive Learning for Generalizable Person Re-identification

Domain generalizable person re-identification aims to apply a trained mo...

Adaptive Domain-Specific Normalization for Generalizable Person Re-Identification

Although existing person re-identification (Re-ID) methods have shown im...

Learning to Optimize Domain Specific Normalization for Domain Generalization

We propose a simple but effective multi-source domain generalization tec...

Epoch-evolving Gaussian Process Guided Learning

In this paper, we propose a novel learning scheme called epoch-evolving ...

Domain-Specific Bias Filtering for Single Labeled Domain Generalization

Domain generalization (DG) utilizes multiple labeled source datasets to ...

Feature-Distribution Perturbation and Calibration for Generalized Person ReID

Person Re-identification (ReID) has been advanced remarkably over the la...

Lifelong Person Re-Identification via Adaptive Knowledge Accumulation

Person ReID methods always learn through a stationary domain that is fix...

Please sign up or login with your details

Forgot password? Click here to reset