CIA: Cluster-Instance Alignment for Unsupervised Day-Night Vehicle Re-Identification

Abstract Cross-time vehicle re-identification (Re-ID), especially across day and night conditions, remains a challenging problem due to drastic illumination variations that lead to significant domain shifts. While existing methods perform well under daytime scenarios, their effectiveness degrades severely in cross-domain settings, and fully supervised solutions demand costly annotations in both domains. In this paper, we introduce a new setting, Unsupervised Day-Night Vehicle Re-Identification (USL-DN-ReID), and propose a novel Cluster-Instance Alignment (CIA) framework to address it. CIA performs dual-level alignment: 1) at the cluster level, a Dictionary-Guided Graph Matching (DGM) module builds a cross-domain topological graph using soft similarities among cluster centers and solves global matching via the Hungarian algorithm; 2) at the instance level, a Multi-Factor Adaptive Alignment (MAA) module introduces a multi-factor adaptive weighting strategy that emphasizes high-confidence pairwise relations while suppressing noise. Together, these components enable robust and scalable cross-domain adaptation without requiring target-domain labels. Extensive experiments conducted on the DN-348 and DN-Wild benchmarks demonstrate the effectiveness and superiority of the proposed CIA framework, setting new state-of-the-art results on both datasets.