Discrete Structure Aggregation and Global-region Query-located Network for Fine-grained Visual Classification

In recent years, the fine-grained classification issue in plants is a hot topical in computer vision. However, most existing fine-grained classification methods focus on finding discriminative regions. These methods overlook the structural information, which is significant to learn more representative structural features and enhance the ability of modules to extract structural features. Moreover, numerous methods use local attention to position regions. They fail to ascertain whether the identified region is the most discriminative on a global scale, which determines whether modules can learn the most discriminative detail features. To address these issues, we propose a Discrete Structure Aggregation and Global-region Querylocated Network (DG-Net) for fine-grained visual classification. Specifically, a Discrete Structure Aggregation Spatial Attention (DSAS) module is proposed to aggregate discrete features and locate the complete object, enhancing the ability of the network to extract the representative structural features. Furthermore, we introduce a Global-region Query-located Attention (GRQL) module, which uses a quadratic attention matrix to assign global importance values for each region, prioritizing the most significant ones and learning the discriminative features. Our method achieves state-of-the-art performance on the 21-Plant, 18-Plant and FGVC-Aircraft datasets. The code is available at https://github.com/0mycode0/code/tree/main/DG-Net.