A Novel Teacher-Student Learning Framework For Occluded Person Re-Identification

Abstract

Person re-identification (re-id) has made great progress in recent years, but occlusion is still a challenging problem which significantly degenerates the identification performance. In this paper, we design a teacher-student learning framework to learn an occlusion-robust model from the full-body person domain to the occluded person domain. Notably, the teacher network only uses large-scale full-body person data to simulate the learning process of occluded person re-id. Based on the teacher network, the student network then trains a better model by using inadequate real-world occluded person data. In order to transfer more knowledge from the teacher network to the student network, we equip the proposed framework with a co-saliency network and a cross-domain simulator. The co-saliency network extracts the backbone features, and two separated collaborative branches are followed by the backbone. One branch is a classification branch for identity recognition and the other is a co-saliency branch for guiding the network to highlight meaningful parts without any manual annotation. The cross-domain simulator generates artificial occlusions on full-body person data under a growing probability so that the teacher network could train a cross-domain model by observing more and more occluded cases. Experiments on four occluded person re-id benchmarks show that our method outperforms other state-of-the-art methods.

References in corpus (9)

• In Defense of the Triplet Loss for Person Re-Identification
• Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking
• Beyond triplet loss: a deep quadruplet network for person re-identification
• HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis
• Learning Deep Context-aware Features over Body and Latent Parts for Person Re-identification
• The Devil is in the Middle: Exploiting Mid-level Representations for Cross-Domain Instance Matching
• Gated Siamese Convolutional Neural Network Architecture for Human Re-Identification
• Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-Free Approach
• Occluded Person Re-identification

Cited by in corpus (7)

• Learning to Disentangle Scenes for Person Re-identification
• High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification
• Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer
• Pose-guided Visible Part Matching for Occluded Person ReID
• Grafted network for person re-identification
• PGGANet: Pose Guided Graph Attention Network for Person Re-identification
• Pose-Guided Feature Learning with Knowledge Distillation for Occluded Person Re-Identification