ReFID: Reciprocal Frequency-aware Generalizable Person Re-identification via Decomposition and Filtering

Abstract

Domain generalization of person re-identification aims to conduct testing across domains that have not been previously encountered, without utilizing target domain data during the training stage. As the number of source domains increases, the relationships between training samples become more complex. This can lead to domain-invariant features that include certain instance-level spurious correlations, which can impact the model’s ability to generalize further. To overcome this limitation, the Reciprocal Frequency-aware Generalizable Person Re-identification method is proposed in this article, which aims to utilize spectral feature correlation learning to transmit frequency component information and generate more discriminative hybrid features. A module called Bilateral Frequency Component-guided Attention is developed to help the network understand high-level semantic and texture information from various frequency features. Furthermore, to reduce the impact of noise from the frequency domain, this article proposes an innovative module called Fourier Noise Masquerade Filtering. This module enhances the portability of frequency domain components while simultaneously suppressing elements that do not contribute to generalization. Extensive experimental results on various datasets demonstrate that our method is effective and superior to the state-of-the-art methods.