  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.
