Multi-Instance Convolutional Neural Network for multi-shot person re-identification

Abstract

This paper tackles the challenging problem of multi-shot person re-identification with Convolutional Neural Network (CNN). As no prior information about how importance each instance plays, it is non-trivial to exploit the interaction information shared by the multi-shot images to help identification. Traditional CNN is in single-shot architecture, then how to utilize the interaction information provided by multi-shot images becomes an important problem to solve. Furthermore, as data augmentation methods are not strictly label-preserving, it increases the difficulty to select discriminative instance for CNN training. In this paper, we propose a weakly supervised CNN framework named Multi-Instance Convolutional Neural Network (MICNN) to solve the aforementioned problem. We develop two paradigms, i.e., Embedding-Space paradigm and Instance-Space paradigm, which re-formulate the person re-identification problem as a multi-instance verification problem with part-based features extracted by neural network. We respectively devise a specific bag-level loss function which incorporates the characteristics of the multi-instance problem for each paradigm. Experiments show that the proposed IS method outperforms many related state-of-the-art techniques on four benchmark datasets: CUHK03, SYSUm, RAiD and Market-1501.