Person Re-Identification using Vision Transformer with Auxiliary Tokens

Abstract

Person Re-Identification (re-ID) is an object re-ID problem that aims to re-identify a person by finding an association between the images of a person captured by multiple cameras. Due to its foundational role in computer-vision based video surveillance applications, it is vital to generate a robust feature embedding to represent a person. CNN-based methods are known for their feature learning abilities, and for many years were a prime choice for a person re-ID. In this theses, we explore a method that takes advantage of auxiliary local tokens and the global tokens of the vision transformer to generate the final feature embedding. We also propose a novel blockwise fine-tuning technique that improves the performance of the Vision Transformer. Our model trained with blockwise fine-tuning achieves $96.6$ rank-1 accuracy and $90.3$ mAP score on the Market-1501 dataset. On the CUHK-03 dataset, it achieves $97.5$ rank-1 accuracy and a $95.03$ mAP score. These performances are comparable to many recently published methods for this problem.