Learning Deep Representations by Mutual Information for Person Re-identification

Abstract

Most existing person re-identification (ReID) methods have good feature representations to distinguish pedestrians with deep convolutional neural network (CNN) and metric learning methods. However, these works concentrate on the similarity between encoder output and ground-truth, ignoring the correlation between input and encoder output, which affects the performance of identifying different pedestrians. To address this limitation, We design a Deep InfoMax (DIM) network to maximize the mutual information (MI) between the input image and encoder output, which doesn't need any auxiliary labels. To evaluate the effectiveness of the DIM network, we propose end-to-end Global-DIM and Local-DIM models. Additionally, the DIM network provides a new solution for cross-dataset unsupervised ReID issue as it needs no extra labels. The experiments prove the superiority of MI theory on the ReID issue, which achieves the state-of-the-art results.

arXiv comment8 pages, 9 figures

References in corpus (12)

• Representation Learning with Contrastive Predictive Coding
• Learning deep representations by mutual information estimation and maximization
• Person Re-identification: Past, Present and Future
• Random Erasing Data Augmentation
• f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
• Pedestrian Alignment Network for Large-scale Person Re-identification
• Deep Representation Learning with Part Loss for Person Re-Identification
• MINE: Mutual Information Neural Estimation
• GLAD: Global-Local-Alignment Descriptor for Pedestrian Retrieval
• Deep Transfer Learning for Person Re-identification
• Looking Beyond Appearances: Synthetic Training Data for Deep CNNs in Re-identification
• Unsupervised Person Re-identification: Clustering and Fine-tuning