Person Re-Identification with a Locally Aware Transformer

TL;DR

This paper introduces a Locally Aware Transformer for person re-identification that leverages local tokens and blockwise fine-tuning, achieving state-of-the-art accuracy on benchmark datasets.

Contribution

The paper proposes a novel LA-Transformer architecture that utilizes local tokens and ensemble classification, along with blockwise fine-tuning, to improve re-ID accuracy.

Findings

Achieves 98.27% rank-1 accuracy on Market-1501

Achieves 98.7% rank-1 accuracy on CUHK03

Outperforms existing state-of-the-art methods

Abstract

Person Re-Identification is an important problem in computer vision-based surveillance applications, in which the same person is attempted to be identified from surveillance photographs in a variety of nearby zones. At present, the majority of Person re-ID techniques are based on Convolutional Neural Networks (CNNs), but Vision Transformers are beginning to displace pure CNNs for a variety of object recognition tasks. The primary output of a vision transformer is a global classification token, but vision transformers also yield local tokens which contain additional information about local regions of the image. Techniques to make use of these local tokens to improve classification accuracy are an active area of research. We propose a novel Locally Aware Transformer (LA-Transformer) that employs a Parts-based Convolution Baseline (PCB)-inspired strategy for aggregating globally enhanced local classification tokens into an ensemble of $\sqrt{N}$ classifiers, where $N$ is the number of patches. An additional novelty is that we incorporate blockwise fine-tuning which further improves re-ID accuracy. LA-Transformer with blockwise fine-tuning achieves rank-1 accuracy of $98.27 \%$ with standard deviation of $0.13$ on the Market-1501 and $98.7\%$ with standard deviation of $0.2$ on the CUHK03 dataset respectively, outperforming all other state-of-the-art published methods at the time of writing.