Re-ID done right: towards good practices for person re-identification

Abstract

Training a deep architecture using a ranking loss has become standard for the person re-identification task. Increasingly, these deep architectures include additional components that leverage part detections, attribute predictions, pose estimators and other auxiliary information, in order to more effectively localize and align discriminative image regions. In this paper we adopt a different approach and carefully design each component of a simple deep architecture and, critically, the strategy for training it effectively for person re-identification. We extensively evaluate each design choice, leading to a list of good practices for person re-identification. By following these practices, our approach outperforms the state of the art, including more complex methods with auxiliary components, by large margins on four benchmark datasets. We also provide a qualitative analysis of our trained representation which indicates that, while compact, it is able to capture information from localized and discriminative regions, in a manner akin to an implicit attention mechanism.

References in corpus (4)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis
• Deep Mutual Learning
• Neural Person Search Machines

Cited by in corpus (8)

• AANet: Attribute Attention Network for Person Re-Identifications
• Style Normalization and Restitution for Generalizable Person Re-identification
• Joint Detection and Tracking in Videos with Identification Features
• Global Distance-distributions Separation for Unsupervised Person Re-identification
• Apparel-invariant Feature Learning for Apparel-changed Person Re-identification
• Learning to Learn in a Semi-Supervised Fashion
• Diversity-Achieving Slow-DropBlock Network for Person Re-Identification
• End-to-End Training of CNN Ensembles for Person Re-Identification