{"type":"ScholarlyArticle","title":"Semantics-Aware Deep Correspondence Structure Learning for Robust Person Re-Identification","authors":[{"name":"Yaqing Zhang","slug":"yaqing-zhang","given":"Yaqing","family":"Zhang","affiliation":""},{"name":"Xi Li","slug":"xi-li-2","given":"Xi","family":"Li","affiliation":""},{"name":"Liming Zhao","slug":"liming-zhao","given":"Liming","family":"Zhao","affiliation":""},{"name":"Zhongfei Zhang","slug":"zhongfei-zhang","given":"Zhongfei","family":"Zhang","affiliation":""}],"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.","doi":null,"url":"https://ijcai.org/proceedings/2016/500","pdf":"https://ijcai.org/proceedings/2016/0500.pdf","markdown":"https://ijcai.org/proceedings/2016/500.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/500","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":500,"pages":{"first":3545,"last":3550},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}