Brownian Distance Covariance Few-shot Image Classification Algorithm Based on Hybrid Attention
BAO Chunmei
WANG Qian
CHEN Wang
LI Zhiling
WANG Bin
WANG Lin
Abstract：In order to solve the problems that the extracted feature of backbone convolutional neural network lacked correlation in few-shot image classification and the channel feature information was easily lost when it was expressed as Brownian distance covariance(BDC),and a image classification algorithm based on hybrid attention-Brownian distance covariance(HA-BDC)was proposed.Firstly,the backbone convolutional neural network was introduced into a weighted nonlocal attention mechanism,which enhanced the feature extraction ability.Secondly,an efficient multiscale attention module with cross-space learning was used to reshape the embedded features so that the new feature tensor could retain the information on each channel.The BDC module was used to represent the new feature tensor as a BDC matrix,the weight matrix of the BDC matrix was obtained through the fully connected layer and finally a logistic regression model was employed for classification.Classification experiments were performed on the mini ImageNet large scale visual recognition challenge(miniImageNet)and tiered ImageNet large scale visual recognition challenge(tieredImageNet)datasets.The results showed that the classification accuracy of the proposed algorithm was improved by 2.83％and 0.77％respectively in the 5-way 1-shot task compared with the simple transfer learning deep Brownian distance convariance(STL DeepBDC)model.The research can be used in the identification of endangered animals and rare diseases with a small amount of data.
Keywords：machine learningfew-shot learningmetric learningBrownian distance covariancehybrid attention mechanismfeature reshaping
Publication Date：2024-12-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 521-527 )