Weakly Supervised Bilinear Convolutional Neural Network for Fine-Grained Vehicle Classification

Fine-grained vehicle classification, which is a key technology within intelligent transportation systems, has been gaining increasing importance with the burgeoning growing number of vehicles. Previous studies have predominantly focused on intricate and distinctive local features. However, in various tasks, it has been proven that global features are of significant importance when they can be effectively integrated with local features in a harmonious manner. So, we consider that a comprehensive consideration of both local and global features is crucial for enhancing classification decisions. Consequently, the paper designs a novel architecture for the task, which combines global and local features to improve classification performance. The architecture consists of two components: the local-feature net and the global-feature net. Specially, for the local feature, we propose an Essential Part Locator module that uses global feature-weighted attention masks to obtain local features, and a Cross-Part Feature Transformer that boosts interactions between local features. Meanwhile, our architecture processes the entire image through an encoder to capture global features and then integrates both global and local features. Experimental results on the Stanford Cars, CompCars, and BoxCars116K datasets demonstrate that the proposed approach surpasses state-of-the-art methods, achieving accuracies of 97.5%, 96.4%, and 92.1%, respectively.