Flexible separable convolution framework, feature extraction method and application thereof in VGG and ResNet

Abstract

The invention discloses a flexible separable convolution framework, a feature extraction method and application thereof in VGG and ResNet. The flexible separable convolution framework comprises a feature map clustering division module, a first convolution operation module, a second convolution operation module, a feature map fusion module and an attention mechanism SE module. The feature map clustering division module divides a feature map into a representation main information feature map and a representation supplementary information feature map; the first convolution operation module performs common convolution operation on the representation main information feature map; the second convolution operation module performs grouping convolution operation on the representation supplementaryinformation feature map; the feature map fusion module firstly splices the convolved feature map, and then adds and activates the convolved feature map and an original feature map; and the attention mechanism SE module multiplies the extracted channel weight by the feature map to generate an output feature map. The method comprises common convolution, grouping convolution, residual branches and anattention mechanism SE, the calculation amount of operation and the parameter amount of the network are reduced while the operation accuracy is guaranteed, and the framework can be used in a neural network convolution layer in a plug and play mode.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a flexible and separable convolution framework and feature extraction method and its application in VGG and ResNet. Background technique [0002] In recent years, deep convolutional neural networks have shown excellent performance on different computer vision tasks such as image recognition, object detection, and semantic segmentation. However, traditional deep convolutional neural networks require a large number of parameters and floating-point operations to achieve satisfactory accuracy, and the model inference time is long. In some real application scenarios, such as mobile or embedded devices, due to the limited memory and computing resources of the device, the actual deployment of traditional deep convolutional neural networks in small devices is difficult; at the same time, the actual application requirements for low latency are difficult. Satisfy. Although the h...

AI Technical Summary

Problems solved by technology

However, traditional deep convolutional neural networks require a large number of parameters and floating-point operations to achieve satisfactory accuracy, and the model inference time is long

In some real application scenarios, such as mobile or embedded devices, due to the limited memory and computing resources of the device, the actual deployment of traditional deep convolutional neural networks in small devices is difficult; at the same time, the actual application requirements for low latency are difficult. Satisfy

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