# Swin-YOLO for Concealed Object Detection in Millimeter Wave Images
## 2 Related Work
### The Attention Mechanism  
The attention mechanism is a structure that focuses on key information and has profound implications on computer vision image processing. By focusing on specific regions of an image with high weights, the attention mechanism simulates human attention and enables neural networks to have perceptual capabilities. Specifically, it reduces the amount of sample data that needs to be processed and increases the feature matching of samples. Earlier, researchers combined Recurrent Neural Network (RNN) models with attention mechanisms [27] for image classification, which achieved correlation expression between features based on solving the problem of gradient explosion in RNN networks. In CNN, a Squeeze-and-Excitation Network (SE-Net) [28] focuses on the relationship between image channels. It weights the features between different channels to emphasize the effective information and suppress the invalid information, and to improve the detection performance of neural networks. The Convolutional Block Attention Module (CBAM) [29], as a lightweight attention module that is easy to deploy, combines channel and spatial attention. It adaptively adjusts the original input feature maps based on the attention feature maps in both channel and spatial dimensions.  
The self-attention mechanism is another form of attention mechanism, which, unlike spatial and channel attention, shows good performance in characterizing context relationships and is widely used in natural language processing (NLP) tasks. In computer vision tasks, a Non-Local network [30] was the first to apply the self-attention mechanism to the field of object detection to address the limitation of convolutional units in local region operations. Self-attention can enhance the acquisition of global information and guide the feature extraction of convolutional neural networks. Moreover, this mechanism can also enhance the global semantic characteristics of the deep network features, strengthening the contextual connection between foreground and background. Visual Transformer (Vit) [31], which is based on pure self-attention mechanisms, has demonstrated that a pure attention network can achieve a classification performance that is comparable to that of CNNs. The network uses a standard Transformer Encoder to globally model image patches. A Swin Transformer [32], on the other hand, combines the advantages of Transformers and CNN, uses a hierarchical structure to obtain information at different scales, and improves the computational efficiency of the Transformer [33] using shifted windows. Swin Transformers perform well in multiple tasks such as detection, classification, and segmentation, filling the gap in the downstream tasks of Vit. Since the concealed object within MMW images are generally small and the MMW image features are more complex, the network requires a better detector to accomplish the detection of MMW concealments. The local information modeling of CNN has been proven effective, while the Swin Transformer can fully extract high-level semantic features of the image and establish context information connections in the image. Therefore, we explore the combination of CNN and Swin Transformers to improve the performance of MMW object detection.