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Semantics-Aware Deep Correspondence Structure Learning for Robust Person Re-identification

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.



Publication

    Yaqing Zhang, Xi Li, Liming Zhao, Zhongfei Zhang. “Semantics-Aware Deep Correspondence Structure Learning for Robust Person Re-identification”. 25th International Joint Conference on Artificial Intelligence (IJCAI). 2016. [paper] [code] [model]

@inproceedings{DCSL2016,
  author = {Yaqing Zhang and Xi Li and Liming Zhao and Zhongfei Zhang},
  title = {Semantics-Aware Deep Correspondence Structure Learning for Robust Person Re-Identification}, 
  booktitle = {Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI)},
  pages = {3545--3551},
  year = {2016},
}

Experimental Results




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