---
title: "Semantics-Aware Deep Correspondence Structure Learning for Robust Person Re-Identification"
authors:
  - "Yaqing Zhang"
  - "Xi Li"
  - "Liming Zhao"
  - "Zhongfei Zhang"
doi: ""
url: "https://ijcai.org/proceedings/2016/500"
pdf: "https://ijcai.org/proceedings/2016/0500.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "3545-3550"
track: ""
keywords:
---

# Semantics-Aware Deep Correspondence Structure Learning for Robust Person Re-Identification

Yaqing Zhang, Xi Li, Liming Zhao, Zhongfei Zhang

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 3545-3550.

## Abstract

In this paper, we propose an end-to-end deep correspondence structure learning (DCSL) approach to address the cross-camera person-matching problem in the person re-identification task. The proposed DCSL approach captures the intrinsic structural information on persons by learning a semantics-aware image representation based on convolutional neural networks, which adaptively learns discriminative features for person identification. Furthermore, the proposed DCSL approach seeks to adaptively learn a hierarchical data-driven feature matching function which outputs the matching correspondence results between the learned semantics-aware image representations for a person pair. Finally, we set up a unified end-to-end deep learning scheme to jointly optimize the processes of semantics-aware image representation learning and cross-person correspondence structure learning, leading to more reliable and robust person re-identification results in complicated scenarios. Experimental results on several benchmark datasets demonstrate the effectiveness of our approach against the state-of-the-art approaches.