Fine-Grained Image Classification Model Based on Bilinear Aggregate Residual Attention.

Due to diversity in local information between categories is relatively subtle in fine-grained image classification tasks, it often causes problems such as insufficient ability of the model to capture discriminative features, and poor interdependence between channels when extracting features. As a result, the network cannot learn the salient and diverse image category features, which ultimately affects the classification performance. Therefore, this paper proposes a bilinear aggregate residual attention network (BARAN). In order to improve the feature capture ability of the network, firstly, based on the original bilinear convolutional neural networks model (B-CNN), the original feature extraction sub-network is transformed into a more learning aggregate residual network. And then, a distraction module is embedded in each aggregate residual block, so that the network focuses on integrating cross-dimensional features, and strengthens the degree of close association between channels in the feature acquisition process. Finally, the fused bilinear feature map is input into the cross-channel attention module, and the discriminative and distinctive sub- components included in the cross- channel attention module are used to further learn more subtle, diverse and mutually exclusive local inter-classes confusing information. Experimental results show that the classification accuracy on the fine-grained image datasets of CUB-200-2011, FGVC-Aircraft and Stanford Cars is 87.9%, 92.9% and 94.7%, which is superior to primary mainstream methods in classification performance. Moreover, the improvement is 0.038, 0.088 and 0.034 compared with the original B-CNN model. [ABSTRACT FROM AUTHOR]