Cross-Correlated Attention Networks for Person Re-Identification
This work addresses person re-identification for surveillance and security applications, representing an incremental improvement over existing attention-based methods.
The paper tackles the problem of person re-identification under challenges like occlusion and viewpoint variations by proposing a Cross-Correlated Attention Network (CCAN) that captures inter-dependencies between attended features, resulting in performance that comfortably outperforms state-of-the-art algorithms.
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.