TIG-CL: Teacher-Guided Individual- and Group-Aware Contrastive Learning for Unsupervised Person Reidentification in Internet of Things

Unsupervised person reidentification (Re-ID) has attracted widespread due to its potential in Internet of Things applications, such as intelligent visual surveillance, it refers to retrieving the same individual across different camera views without using labeled data. To tackle the problem, a prevalent technique adopted by existing methods involves generating pseudo labels through clustering algorithms. However, this approach can result in merging individuals with different identities into the same group (i.e., cluster) during the training process. As a result, the resulting group centers may obscure the inherent characteristics of individual identities, thereby hindering the model from learning discriminative representations. To address the issue, we present a teacher-guided individual- and group-aware contrastive learning framework. Specifically, we propose a departure from the traditional approach of relying solely on contrastive learning between individual features and their corresponding group centers. Instead, we also exploit the relationship among individuals to construct contrast pairs and facilitate the learning of more discriminative features. This strategy enables the model to learn more about the individual characteristics that distinguish different persons, thus enhancing its ability to reidentify individuals accurately. Moreover, our method introduces a novel hybrid distillation module that enables simultaneous probability distillation at the group level and relationship distillation at the individual level. Guided by the teacher model, this module leads to improved feature representations of the student model. Extensive experimental results verify the effectiveness of our approach on four popular Re-ID data sets. The code will be made publicly available.