# Attention Block Based on Binary Pooling
## 1 Introduction  
Computer vision is widely recognized as a critical component of artificial intelligence, as it enables machines to \"see\" and understand the physical world. Over the past two decades, computer vision has undergone rapid development, with the emergence of numerous theories and methods, resulting in significant progress in various core issues. Over the years, with the advancement of deep learning technology, image classification has been widely studied and applied successfully in several domains such as medical image analysis, autonomous driving, security monitoring, and garbage classification, among others. [PERSON] [1] indicated that artificial intelligence demonstrates promising outcomes in the field of medicine. Particularly in radiation therapy, studies by [PERSON] [2], [PERSON] [3], [PERSON] [4], and [PERSON] [5] have all shown that image classification technology can assist doctors in identifying patient conditions. The potential of image classification technology is enormous. Historically, image classification relied on statistical learning techniques such as Bayesian classifiers and K-Nearest Neighbors for feature extraction and pattern recognition. However, these approaches were limited in handling larger datasets and could not scale to larger tasks. The advent of artificial neural networks, particularly CNNs, revolutionized image classification, providing a flexible and powerful method for training and reasoning with large datasets. CNNs have become the gold standard for image classification and have resulted in significant advancements in image processing and computer vision. The limitations in the structure and learning algorithms of early neural network models hindered their ability to effectively address complex image processing problems. However, in the late 1980s and early 1990s, [PERSON] [6] first proposed a convolutional neural network model combining convolution operation, pooling operation, and nonlinear activation function, which can effectively process image data. In the 2012 ImageNet large-scale visual recognition challenge, AlexNet [7] achieved a significant result, surpassing traditionalmachine learning methods. Since then, several well-established CNN models, including VGGNet [8], GoogLeNet [9], ResNet [10], and DenNet [11], have shown impressive performance on various image classification tasks. However, these models are computationally expensive and require significant memory resources, making them challenging to deploy on mobile devices. To address this issue, researchers have developed lightweight models such as SqueezedNet [12], MobileNet [13], ShuffleNet [14], and EfficientNet [15], which are designed to be easily deployable on mobile devices. There are a lot of techniques for ameliorating the model performance, which encompass enhancements of the model structure, augmentation of the training data, and adoption of transfer learning approaches. The success achieved by the Vision Transformer [16] has brought attention to the utility of attention mechanisms in facilitating feature extraction. The attention mechanism is a technique for selectively attending to the most informative regions in an image while disregarding irrelevant regions. This approach is inspired by the human visual system's ability to efficiently analyze and comprehend complex scenes through selective attention. The potential of this mechanism has led researchers to explore its application in computer vision systems to enhance their performance. Specifically, the attention mechanism can be viewed as a dynamic selection process that adaptively weights input features according to their importance, which enables the network to effectively focus on the most informative and relevant aspects of the input. The beginning of attention mechanisms in computer vision is the Recurrent Attention Model (RAM) [17], which combined deep neural networks with attention mechanisms to recurrently predict important regions in an image and update the entire network.