基于稀疏化双线性卷积神经网络的细粒度图像分类

doi:10.16451/j.cnki.issn1003-6059.201904006

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Abstract 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.

Key words： Fine-Grained Visual Recognition Bilinear Convolutional Neural Network(B-CNN)
Overfitting Network Sparsity Network Pruning

Received: 03 January 2019

ZTFLH:

TP 391

Fund:Supported by National Natural Science Foundation of China(No.61673276,61703277)

About author:: MA Li, master student. His research interests include computer vision and image processing.WANG Yongxiong(Corresponding author), Ph.D., professor. His research inte-rests include intelligent robot and vision.

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Cite this article:

MA Li,WANG Yongxiong. Fine-Grained Visual Classification Based on Sparse Bilinear Convolutional Neural Network[J]. , 2019, 32(4): 336-344.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201904006 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I4/336

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