Channel Expansion Convolutional Network for Image Classification

With the continuous evolution of research on convolutional neural networks, it is an efficient and fashionable method to introduce attention mechanism into the convolutional structure. The channel attention designed in SENet has made a great contribution to the promotion of the attention convolution model. However, our research found that SENet focuses on certain feature channels rather than objects in the channels. It will simultaneously enhance or weaken the target objects and background information in a certain channel. On the basis of the channel attention convolution network, we first perform channel sorting and group convolution on the feature map, and expand each group to β times the original feature channel during the group convolution process to construct a channel expansion convolution network (CENet), where β is an array used to represent the channel expansion coefficient. CENet captures the attention of objects in the feature channel while expanding the proportion of features in the relatively important channel. Furthermore, we improved the structure of CENet and merged it into the intra-layer multi-scale convolutional model to construct an object-level attention multi-scale convolutional neural network (OAMS-CNN). We have conducted a large number of experiments on four data sets, CIFAR-10, CIFAR-100, FGVC-Aircraft and Stanford Cars. The experimental results show that our proposed new object-level attention convolution model has achieved good image classification results.