Unsupervised Vehicle Re-Identification via Self-supervised Metric Learning using Feature Dictionary

TL;DR

This paper introduces an unsupervised vehicle re-identification method that leverages self-supervised metric learning with a feature dictionary, eliminating the need for labeled datasets and improving discriminative feature learning.

Contribution

It proposes a novel self-supervised approach combining dictionary-based positive label mining and triplet loss to enhance unsupervised vehicle Re-ID performance.

Findings

Achieves promising vehicle Re-ID results without labeled data

Effectively refines feature discriminativeness through iterative DPLM and DTL

Demonstrates robustness in unsupervised settings

Abstract

The key challenge of unsupervised vehicle re-identification (Re-ID) is learning discriminative features from unlabelled vehicle images. Numerous methods using domain adaptation have achieved outstanding performance, but those methods still need a labelled dataset as a source domain. This paper addresses an unsupervised vehicle Re-ID method, which no need any types of a labelled dataset, through a Self-supervised Metric Learning (SSML) based on a feature dictionary. Our method initially extracts features from vehicle images and stores them in a dictionary. Thereafter, based on the dictionary, the proposed method conducts dictionary-based positive label mining (DPLM) to search for positive labels. Pair-wise similarity, relative-rank consistency, and adjacent feature distribution similarity are jointly considered to find images that may belong to the same vehicle of a given probe image. The results of DPLM are applied to dictionary-based triplet loss (DTL) to improve the discriminativeness of learnt features and to refine the quality of the results of DPLM progressively. The iterative process with DPLM and DTL boosts the performance of unsupervised vehicle Re-ID. Experimental results demonstrate the effectiveness of the proposed method by producing promising vehicle Re-ID performance without a pre-labelled dataset. The source code for this paper is publicly available on `https://github.com/andreYoo/VeRI_SSML_FD.git'.