Fine-Grained Visual Classification Network Based on Fusion Pooling and Attention Enhancement

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. © 2023 Journal of Pattern Recognition and Artificial Intelligence. All rights reserved.