基于融合池化和注意力增强的细粒度视觉分类网络

doi:10.16451/j.cnki.issn1003-6059.202307007

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Abstract The core of fine-grained visual classification is to extract image discriminative features.In most of the existing methods, attention mechanisms are introduced to focus the network on important regions of the object.However, this kind of approaches can only locate the salient feature and cannot cover all discriminative features. Consequently, different categories with similar features are easily confusing. Therefore, a fine-grained visual classification network based on fusion pooling and attention enhancement is proposed to obtain comprehensive discriminative features. At the end of the network, a fusion pooling module is designed with a three-branch structure to obtain multi-scale discriminative features. The three-branch structure includes global average pooling, global top-k pooling and the fusion of the previous two. In addition, an attention enhancement module is proposed to gain two more discriminative images through attention grid mixing module and attention cropping module under the guidance of attention maps. Experiments on fine-grained image datasets, CUB-200-2011, Stanford Cars and FGVC-Aircraft, verify the high accuracy rate and strong competitiveness of the proposed network.

Key words： Fine-Grained Visual Classification Fusion Pooling Attention Mechanism Data Augmentation

Received: 23 May 2023

ZTFLH:

TP391

Fund:Sichuan Scientific Innovation Fund(No. 2022JDRC0009), Natural Science Starting Project of Southwest Petroleum University(No.2022QHZ023)

Corresponding Authors: ZHANG Xingpeng, Ph.D., lecturer. His research interests include image recognition, object detection and medical image segmentation.

About author:: XIAO Bin, master, professor. His research interests include pattern recognition. GUO Jingwei, master student. His research interests include fine-grained visual classification. WANG Min, master, professor. Her research interests include artificial intelligence and signal analysis and processing.

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Cite this article:

XIAO Bin,GUO Jingwei,ZHANG Xingpeng等. Fine-Grained Visual Classification Network Based on Fusion Pooling and Attention Enhancement[J]. Pattern Recognition and Artificial Intelligence, 2023, 36(7): 661-670.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202307007 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2023/V36/I7/661

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