Mask-Aware Pseudo Label Denoising for Unsupervised Vehicle Re-Identification

Abstract

As a significant part of Intelligent Transportation System (ITS), vehicle Re-Identification (Re-ID) aims to retrieve all target vehicle images captured from non-overlapping cameras. Though the Re-ID methods based on supervised learning have achieved rapid progress, they are still difficult to be applied in real scenarios due to the domain bias between the training set and real scenarios. Recently, methods based on unsupervised learning have been proposed to address the problem of domain bias by exploring techniques of pseudo-label generation. However, these methods suffer from pseudo-label noise. To solve this problem, we propose the Mask-Aware Pseudo Label Denoising framework (MAPLD) consisting of three key components, i.e., Mask-Aware Feature Extraction (MAFE), Adaptive Threshold Neighborhood Consistency (ATNC), and Compact Loss (CL). Firstly, the MAFE is proposed to improve the distinguishability of feature representation and widen the gap in feature space among vehicles with different IDs. Next, the ATNC is introduced to filter out pseudo-label noise of hard negative samples by comparing the image ID of the samples in their neighborhood set i.e., neighborhood consistency. Moreover, the threshold of neighborhood consistency is adaptively adjusted according to feature similarity ranking, which is robust to hyper-parameter variation. Finally, consisting of regression term and compact term, the CL is designed to drive the cluster more compact and alleviate the impact of outliers of hard positive samples. Extensive experiments on VeRi-776 and VeRi-Wild datasets demonstrate that MAPLD can generate reliable pseudo-labels and achieve superior performance in unsupervised target-only and unsupervised domain adaptation tasks.