Feature channel interaction long-tailed image classification model based on dual attention

In the real world, the data distribution often presents a long tail distribution, and the imbalance of data will lead to the model learning bias to the head class. To address the influence of long tail distribution on image classification, this paper proposes a feature channel interactive long tail image classification model based on dual attention. Firstly, the dual attention module is used to capture the autocorrelation and spatial dimension information of the feature map, and the enhanced image is obtained by transformation and class activation map. After that, image preprocessing is performed on the enhanced data set to reduce the over-fitting of the model to the head, and the features that are more conducive to tail classification are obtained through learning. Finally, by interacting with the local channels adjacent to the features, the correlation between the channels is extracted to obtain more robust features. The method achieves good performance on CIFAR10-LT, CIFAR100-LT and ImageNet datasets, which proves the effectiveness of the model.