Cross-Correlated Attention Networks for Person Re-Identification

作者：

Highlights：

• Modeling the inherentspatial relations between different attended regions within the deep architecture.

• Joint end-to-end cross correlated attention and representational learning.

• State-of-the-art results in terms of mAP and Rank-1 accuracies across several challenging datasets.

摘要

•Modeling the inherentspatial relations between different attended regions within the deep architecture.•Joint end-to-end cross correlated attention and representational learning.•State-of-the-art results in terms of mAP and Rank-1 accuracies across several challenging datasets.

论文关键词：Attention,Feature extraction,Cross correlation,Person Re-Identification,Surveillance

论文评审过程：Received 3 May 2020, Accepted 10 May 2020, Available online 25 May 2020, Version of Record 9 June 2020.

论文官网地址：https://doi.org/10.1016/j.imavis.2020.103931