PersonViT: Large-scale Self-supervised Vision Transformer for Person Re-Identification

TL;DR

PersonViT introduces a large-scale self-supervised Vision Transformer approach for person re-identification, effectively combining masked image modeling and contrastive learning to extract detailed local and global features, achieving state-of-the-art results.

Contribution

The paper presents a novel self-supervised ViT-based method, PersonViT, that effectively captures local and global features for person ReID without requiring extensive annotations.

Findings

Achieves state-of-the-art performance on multiple benchmark datasets.

Effectively combines masked image modeling with contrastive learning.

Demonstrates strong generalization and scalability in person ReID tasks.

Abstract

Person Re-Identification (ReID) aims to retrieve relevant individuals in non-overlapping camera images and has a wide range of applications in the field of public safety. In recent years, with the development of Vision Transformer (ViT) and self-supervised learning techniques, the performance of person ReID based on self-supervised pre-training has been greatly improved. Person ReID requires extracting highly discriminative local fine-grained features of the human body, while traditional ViT is good at extracting context-related global features, making it difficult to focus on local human body features. To this end, this article introduces the recently emerged Masked Image Modeling (MIM) self-supervised learning method into person ReID, and effectively extracts high-quality global and local features through large-scale unsupervised pre-training by combining masked image modeling and discriminative contrastive learning, and then conducts supervised fine-tuning training in the person ReID task. This person feature extraction method based on ViT with masked image modeling (PersonViT) has the good characteristics of unsupervised, scalable, and strong generalization capabilities, overcoming the problem of difficult annotation in supervised person ReID, and achieves state-of-the-art results on publicly available benchmark datasets, including MSMT17, Market1501, DukeMTMC-reID, and Occluded-Duke. The code and pre-trained models of the PersonViT method are released at \url{https://github.com/hustvl/PersonViT} to promote further research in the person ReID field.