# Learning a Hierarchical Global Attention for Image Classification
## 1 Introduction  
To classify the image material on the internet, the deep learning/neural network methodology is the most optimal but also the costliest of all computer vision methods. Convolutional neural network (CNN) has been proved as a powerful tool for different computer vision tasks [1]. In CNN, filters extract the information from features with learned adaptive weights and bias [2]. By building the network deeper or wider, the accumulation of filters demonstrates a significant improvement in feature representation. Different from fully connection networks, the filters in CNN have fixed receptive fields to concentrate on the local information from features, saving the parameters and making it possible to build the network deeper [3]. Recently, researchers concentrate on well-designed network architectures for efficient feature exploration [4; 5]. The elaborate network designs are composed of depth-wise and channel-wise convolutional layers and effectively exploit the features with modified filters [6]. As the receptive fields and network depths are fixed, these works almost neglect the global information for holistic consideration. Attention mechanism has been introduced to address this issue [7; 8]. As a weighting component for importance distribution, attentions are usually calculated as non-negative feature maps from a Sigmoid activation. To consider the global information, global average pooling is introduced to measure the information from the entire feature map [7]. After pooling, the feature maps will be compressed into a fixed size vector according to the channels. Then, fully connection layers with activation functionsare utilized for nonlinear exploration. This channel-wise attention mechanism applies pooling to information evaluation, which treats the spatial information of features equally. In fact, the intrinsic property of pooling operation neglects the diversity of spatial information. Another global attention mechanism termed as non-local attention is proposed based on the matrix multiplication [9]. Due to the characteristic of matrix multiplication, all the spatial information will be considered for holistic consideration. Non-local attention has been proved as an effective design for network representation improvement. However, the matrix multiplication will result in a large memory consumption and high computation complexity. All of this will be further elaborated/introduced in the Related Works. To reduce the CNN costs and provide better classification performance, we devise an improvement of attention mechanism for comprehensive information consideration. In the proposed hierarchical global attention (HGA) mechanism, multi-scale structure has been proved as an effective mechanism for hierarchical information exploration. We hold to the notion that features from different scales obtain various information, and the multi-scale structure can comprehensively exploit the features for better representation. Global average pooling is a suitable operation to estimate the information from different feature maps, which lacks the spatial-wise consideration. By combining the global average pooling and multi-scale structure, the attention mechanism can learn a comprehensive relations among different feature maps. Then, the nonlinear exploration will find a more adaptive representation of features. The holistic design of proposed HGA mechanism is shown in Figure 1. There are few parameters and restricted computation complexity in HGA, which makes it possible for flexible plug-and-play in exist effective network structures. To address this point, we provide several patterns for flexibly applying HGA to the existing network backbones. Experimental results shows HGA can boost the image classification capacity for different advanced network structures. The contributions of our work can be summarized as follows. * We propose a hierarchical global attention (HGA) mechanism for comprehensive information consideration.