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.

当考虑人的重新识别任务时，深度神经网络需要在存在遮挡，背景混乱，姿势和视点变化的情况下做出可靠的推断（仅举几例）。注意力机制最近已被证明在某种程度上成功地应对了上述挑战。但是，以前的设计无法捕获所涉及功能之间的固有相互依存关系。导致注意力块之间的相互作用受到限制。在本文中，我们提出了一个新的注意力模块，称为交叉相关注意力（CCA）; 旨在通过最大化不同参与区域之间的信息增益来克服此类限制。此外，我们还提出了一种新颖的深度网络，该网络利用不同的注意力机制来学习人像的鲁棒和区分性表示。产生的模型称为交叉相关注意力网络（CCAN）。大量的实验表明，CCAN可以切实地胜过当前的最新算法。

对深层架构中不同参与区域之间的固有空间关系进行建模。联合的端到端交叉相关的注意力和表征学习。在几个具有挑战性的数据集中，根据mAP和Rank-1精度得出的最新结果。