Cross-Correlated Attention Networks for Person Re-Identification

• URL: http://arxiv.org/abs/2006.09597v1
• Date: Wed, 17 Jun 2020 01:47:23 GMT
• Title: Cross-Correlated Attention Networks for Person Re-Identification
• Authors: Jieming Zhou, Soumava Kumar Roy, Pengfei Fang, Mehrtash Harandi, Lars Petersson
• Abstract summary: We propose a new attention module called Cross-Correlated Attention (CCA) CCA aims to overcome such limitations by maximizing the information gain between different attended regions. We also propose a novel deep network that makes use of different attention mechanisms to learn robust and discriminative representations of person images.
• Score: 34.84287025161801
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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