TRET: Two Stream-Based Regionally Enhanced Transformers for Person Re-Identification

Abstract

Person Re-IDentification (ReID) is a pivotal method for pedestrian tracking and retrieval. This research is inherently challenged by large changes in intra-class or small changes in inter-class. To address this challenge, many researchers have recently introduced transformer-based models, which have shown excellent results. The primary objective of these models is to generate robust features that effectively distinguish between classes and enable generalization. However, existing methods still suffer from class discrimination due to unnecessary noise, including the background. To overcome this limitation, we propose a novel approach called Two stream-based Regionally Enhanced Transformers (TRET) that focuses on the target to be identified. To concentrate on the target region, the TRET utilizes a structure that leverages the pedestrian mask. Furthermore, the proposed model generalizes well by utilizing Contrastive Language-Image Pretraining as the backbone. Finally, our proposed model achieves state-of-the-art performance on the public datasets.