Fine-Grained Visual Classification Based on Sparse Bilinear Convolutional Neural Network

The overfitting problem of bilinear convolutional neural network(B-CNN) for fine-grained visual recognition is caused by the large number of parameters and its complex structure. In this paper, a sparse B-CNN is proposed to handle the problem. Firstly, a scaling factor is introduced into each feature channel of B-CNN, and regularization of sparsity is applied to the scaling factors during the training. Then, the feature channels in B-CNN with low contribution to the final classification are identified by small scaling factors. Finally, these channels are pruned in a certain proportion to prevent overfitting and increase the significance of key features. The learning of sparse B-CNN is weakly supervised and end-to-end. The verification experiments on FGVC-aircraft, Stanford dogs and Stanford cars fine-grained image datasets show that the accuracy of sparse B-CNN is higher than that of the original B-CNN. Moreover, compared with other advanced algorithms for fine-grained visual recognition, the performance of sparse B-CNN is same or even better. 2019, Science Press. All right reserved.