Transformer Based Multi-Grained Features for Unsupervised Person Re-Identification

TL;DR

This paper introduces a novel transformer-based approach for unsupervised person re-identification that extracts multi-grained features and employs contrastive learning, significantly improving performance over existing methods.

Contribution

It proposes a dual-branch Vision Transformer architecture with multi-grained feature extraction and a contrastive learning framework tailored for unsupervised person Re-ID.

Findings

Outperforms state-of-the-art unsupervised Re-ID methods on multiple datasets.

Effectively mitigates the performance gap between unsupervised and supervised Re-ID.

Demonstrates the effectiveness of multi-grained features from transformers in unsupervised learning.

Abstract

Multi-grained features extracted from convolutional neural networks (CNNs) have demonstrated their strong discrimination ability in supervised person re-identification (Re-ID) tasks. Inspired by them, this work investigates the way of extracting multi-grained features from a pure transformer network to address the unsupervised Re-ID problem that is label-free but much more challenging. To this end, we build a dual-branch network architecture based upon a modified Vision Transformer (ViT). The local tokens output in each branch are reshaped and then uniformly partitioned into multiple stripes to generate part-level features, while the global tokens of two branches are averaged to produce a global feature. Further, based upon offline-online associated camera-aware proxies (O2CAP) that is a top-performing unsupervised Re-ID method, we define offline and online contrastive learning losses with respect to both global and part-level features to conduct unsupervised learning. Extensive experiments on three person Re-ID datasets show that the proposed method outperforms state-of-the-art unsupervised methods by a considerable margin, greatly mitigating the gap to supervised counterparts. Code will be available soon at https://github.com/RikoLi/WACV23-workshop-TMGF.