Abstract
To classify the image material on the internet, the deep learning methodology, especially deep neural network, is the most optimal and costliest method of all computer vision methods. Convolutional neural networks (CNNs) learn a comprehensive feature representation by exploiting local information with a fixed receptive field, demonstrating distinguished capacities on image classification. Recent works concentrate on efficient feature exploration, which neglect the global information for holistic consideration. There is large effort to reduce the computational costs of deep neural networks. Here, we provide a hierarchical global attention mechanism that improve the network representation with restricted increase of computation complexity. Different from nonlocal-based methods, the hierarchical global attention mechanism requires no matrix multiplication and can be flexibly applied in various modern network designs. Experimental results demonstrate that proposed hierarchical global attention mechanism can conspicuously improve the image classification precision—a reduction of 7.94% and 16.63% percent in Top 1 and Top 5 errors separately—with little increase of computation complexity (6.23%) in comparison to competing approaches.
Highlights
To classify the image material on the internet, the deep learning/neural network methodology is the most optimal and the costliest of all computer vision methods
Different from fully connection networks, the filters in Convolutional neural networks (CNNs) 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]
As the receptive fields and network depths are fixed, these works almost neglect the global information for holistic consideration
Summary
To classify the image material on the internet, the deep learning/neural network methodology is the most optimal and the costliest of all computer vision methods. Convolutional neural network (CNN) has been proved as a powerful tool for different computer vision tasks [1]. 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]. 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
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