Unsupervised Person Re-Identification with Multi-Label Learning Guided Self-Paced Clustering

TL;DR

This paper introduces a novel unsupervised person re-identification framework called MLC that combines multi-scale features, multi-label learning, and self-paced clustering to improve discriminative feature learning without annotations.

Contribution

The paper proposes a new framework integrating multi-scale features, multi-label assignment, and self-paced clustering for unsupervised person Re-ID, outperforming previous methods.

Findings

MLC achieves state-of-the-art results on large-scale Re-ID benchmarks.

Multi-scale features improve similarity measurement accuracy.

Self-paced clustering effectively reduces noisy samples.

Abstract

Although unsupervised person re-identification (Re-ID) has drawn increasing research attention recently, it remains challenging to learn discriminative features without annotations across disjoint camera views. In this paper, we address the unsupervised person Re-ID with a conceptually novel yet simple framework, termed as Multi-label Learning guided self-paced Clustering (MLC). MLC mainly learns discriminative features with three crucial modules, namely a multi-scale network, a multi-label learning module, and a self-paced clustering module. Specifically, the multi-scale network generates multi-granularity person features in both global and local views. The multi-label learning module leverages a memory feature bank and assigns each image with a multi-label vector based on the similarities between the image and feature bank. After multi-label training for several epochs, the self-paced clustering joins in training and assigns a pseudo label for each image. The benefits of our MLC come from three aspects: i) the multi-scale person features for better similarity measurement, ii) the multi-label assignment based on the whole dataset ensures that every image can be trained, and iii) the self-paced clustering removes some noisy samples for better feature learning. Extensive experiments on three popular large-scale Re-ID benchmarks demonstrate that our MLC outperforms previous state-of-the-art methods and significantly improves the performance of unsupervised person Re-ID.