Semantics Aware Deep Correspondence Structure Learning for Robust Person Re Identification
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.
IJCAI