Camera Style Alignment for Unsupervised Person Re-Identification

Abstract

The purpose of unsupervised person re-identification (Re-ID) is to improve the recognition performance of the model without using any labeled Re-ID datasets. Recently, camera differences and noisy labels have emerged as critical factors hindering the improvement of unsupervised Re-ID performance. To address these issues, we propose a camera style alignment (CSA) method. In CSA, we first devise the feature mean clustering (FM-clustering) algorithm, which is based on the average features for clustering to mitigate the impact of camera differences on the clustering results. Subsequently, we design dual-cluster consistency refinement (DCR), which assesses the reliability of pseudo-labels from the perspective of clustering consistency, thereby reducing the influence of noisy labels. In addition, we introduce style-aware invariance loss and camera-aware invariance loss to achieve camera style-invariant learning from different aspects. Style-aware invariance loss will improve the similarity between samples and their style-transferred counterparts, and camera-aware invariance loss will improve the similarity between positive samples of different cameras. The experimental results on the Market-1501 and MSMT17 datasets show that the performance of CSA exceeds the existing fully unsupervised Re-ID and unsupervised domain adaptation Re-ID methods.