# CNN Attention Enhanced ViT Network for Occluded Person Re-Identification
###### Abstract  
Person re-identification (ReID) is often affected by occlusion, which makes most of the features extracted by ReID models contain a lot of identity-independent noise. Recently, the use of Vision Transformer (ViT) has enabled significant progress in various visual artificial intelligence tasks. However, ViT suffers from insufficient local information extraction capability, which should be of concern to researchers in the field of occluded ReID. This paper conducts a study to exploit the potential of attention mechanisms to enhance ViT in ReID tasks. In this study, an Attention Enhanced ViT Network (AET-Net) is proposed for occluded ReID. We use ViT as the backbone network to extract image features. Even so, occlusion and outlier problems still exist in ReID. Then, we combine the spatial attention mechanism into the ViT architecture, by which we enhance the attention of ViT patch embedding vectors to important regions. In addition, we design a MultiFeature Training Module to optimize the network by the construction of multiple classification features and calculation of the multi-feature loss to enhance the performance of the model. Finally, the effectiveness and superiority of the proposed method are demonstrated by broad experiments on both occluded and non-occluded datasets. person re-identification; vision transformer; artificial intelligence; attention mechanism; classification +
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ReID datasets, some researchers have done in-depth research on data sampling, weakly supervised learning and unsupervised learning [12; 13; 14]. In recent years, with the help of convolutional neural networks, supervised learning models have been broken through in solving the occlusion ReID problem [2; 3; 4; 5; 6; 7; 8; 9]. Some researchers extracted local regions of humans by combining with target segmentation [15; 16; 17] and semantic analysis [2; 7; 18; 19; 20; 21] models to accomplish identity matching, but the introduction of additional models made the cost of occluded ReID much higher. Additionally, other researchers also match detailed information by region segmentation, but such an approach does not work well in the case of occlusion [15; 18; 22; 23]. Because of the randomness, occlusions make these works often fail to match during model identification. Furthermore, lots of works have introduced attention mechanisms into the ReID model in order to filter more significant parts from a huge amount of information, aiming to focus the attention of the model to identify information and ignore secondary information [5; 15; 16; 24; 25; 26]. It has been studied that CNNs use convolution to limit the extraction of image features by the model to small regions, and the use of down-sampling modules would tend to lose the local details of humans [24]. In recent years, the Transformer, popular in Natural Language Processing (NLP) [27; 28], was introduced to the ReID task, achieving comparable performance to CNN-based methods with the help of a self-attention mechanism [5; 24; 29]. However, ViT suffers from insufficient local information extraction capability [30; 31], which should be concerning for researchers in the field of occluded ReID. We hope to improve ViT's ability to focus on important areas by incorporating CNN's spatial attention mechanism. This method uses the convolution process to extract local importance distribution, similar to how the CNN attention mechanism enhances attention to important information. In addition, the channel attention of CNN can extract the channel distribution state of the current feature vector, and it can enhance the attention of the model to the important channel features according to the backpropagation algorithm. Based on the above, this work proposed an Attention Enhanced ViT Network (AET-Net) to address the challenges of ReID occlusion, the general procedure is shown in Figure 2. Additionally, the specific methodology is presented in Section 3, following the related works of Section 2. Thereafter the experimental settings and results are shown in Section 4.