# A Novel Dual Mixing Attention Network for UAV-Based Vehicle Re-Identification
###### Abstract
The channel attention mechanism can effectively enhance essential channels, but it cannot deal with the problem of slight inter-class similarity. Spatial attention mechanisms can selectively amplify or suppress features in specific regions spatially, but they ignore the relationship between channels. To overcome the shortcomings of a  
Figure 1: **Comparison of two types of vehicle images. ** There is a significant difference between the vehicle from the UAV perspective and the vehicle from the fixed camera. The vehicle under the fixed camera shooting angle is relatively fixed. In the view of UAVs, the shooting angles of cars are changeable, and there are many top–down shooting angles. (**a**) Surveillance cameras; (**b**) UAV cameras. single attention mechanism, recent studies have begun to combine channel and spatial attention [19; 20; 21]. Such a hybrid attention mechanism can consider the relationship between channel and space at the same time to better capture the critical information in the input feature tensor. By introducing multiple branches of the attention mechanism or fusing different attention weights, the interaction between features can be modeled more comprehensively. Shuffle attention (SA) [19] divides molecular channels to extract key channel features and local spatial fusion features, with each subchannel acquiring channel and spatial fusion attention. The bottleneck attention module (BAM) [20] is a technique that generates an attention map through two distinct pathways: channel and spatial. On the other hand, the dual attention network (DANet) [21] incorporates two different types of attention modules on dilated fully convolutional networks (FCNs). These attention modules effectively capture semantic dependencies in both spatial and channel dimensions. Most methods make the input feature map directly pass through the fused attention. At the same time, SA [19] can provide richer feature representation by dividing the subchannels, which better capture the structure and associations in images or other data. However, the SA [19] method of dividing the channel into subchannels mainly focuses on weighting the input features in the channel dimension, ignoring the possible details in the spatial dimension. For that, our proposed DMAM, which combines SMA with CMA, in which the original feature is divided according to the dimensions of spatial and channel to obtain multiple subspaces. Each sub-feature map is processed independently, and the features of different channels and local regions can be extracted so that the network can better associate local features with the whole feature. Then, a learnable weight is applied to capture the dependencies between local features in the mixture space. In conclusion, the features extracted from multiple subspaces are merged to enhance their comprehensive interaction. This approach enables the extraction of more resilient features and leads to improved recognition accuracy. The key contributions of this method are outlined as follows:  
* We introduce a novel DMANet designed to handle the challenges of unmanned aerial vehicle UAV-based vehicle ReID. DMANet effectively addresses issues related to shooting angles, occlusions, top-down features, and scale variations, resulting in enhanced viewpoint robust feature extraction. * Our proposed DMAM employs SMA and CMA to capture pixel-level pairwise relationships and channel dependencies. This modular design fosters comprehensive feature interactions, improving discriminative feature extraction under varying viewpoints. * The versatility of DMAM allows its seamless integration into existing backbone networks at varying depths, significantly enhancing vehicle discrimination performance. Our approach demonstrates superior performance through extensive experiments compared to representative methods in the UAV-based vehicle re-identification task, affirming its efficacy in challenging aerial scenarios. The structure of the paper will be as follows: In Section 2, a comprehensive review and discussion of related studies is presented.