Cross-Correlated Attention networks for person re-identification

Jieming Zhou, Soumava Kumar Roy, Pengfei Fang, Mehrtash Harandi, Lars Petersson

Research output: Contribution to journalArticleResearchpeer-review

17 Citations (Scopus)


Deep neural networks need to make robust inference in the presence of occlusion, background clutter, pose and viewpoint variations -to name a few- when the task of person re-identification is considered. Attention mechanisms have recently proven to be successful in handling the aforementioned challenges to some degree. However previous designs fail to capture inherent inter-dependencies between the attended features; leading to restricted interactions between the attention blocks. In this paper, we propose a new attention module called Cross-Correlated Attention (CCA); which aims to overcome such limitations by maximizing the information gain between different attended regions. Moreover, we also propose a novel deep network that makes use of different attention mechanisms to learn robust and discriminative representations of person images. The resulting model is called the Cross-Correlated Attention Network (CCAN). Extensive experiments demonstrate that the CCAN comfortably outperforms current state-of-the-art algorithms by a tangible margin. 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.

Original languageEnglish
Article number103931
Number of pages8
JournalImage and Vision Computing
Publication statusPublished - Aug 2020


  • Attention
  • Cross correlation
  • Feature extraction
  • Person Re-Identification
  • Surveillance

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