Improving person re-identification by attribute and identity learning
作者：
Highlights：
• We annotate attribute labels on two large-scale person re-identification datasets.
• We propose APR to improve re-ID by exploiting global and detailed information.
• We introduce a module to leverage the correlation between attributes.
• We speed-up the retrieval of re-ID by ten times with only a 2.92% accuracy drop.
• We achieve competitive re-ID performance with the state-of-the-art methods.
摘要
•We annotate attribute labels on two large-scale person re-identification datasets.•We propose APR to improve re-ID by exploiting global and detailed information.•We introduce a module to leverage the correlation between attributes.•We speed-up the retrieval of re-ID by ten times with only a 2.92% accuracy drop.•We achieve competitive re-ID performance with the state-of-the-art methods.
论文关键词：Person re-identification,Attribute recognition
论文评审过程：Received 15 May 2018, Revised 1 April 2019, Accepted 5 June 2019, Available online 6 June 2019, Version of Record 19 June 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.06.006
