Object Decoupling with Graph Correlation for Fine-Grained Image Classification

Fine-grained image classification has drawn increasing attention as it is much closer to practical applications than generic image classification. The majority of current fine-grained approaches locate the discriminative regions and leverage the features of these regions for classification as their magic weapons. However, these approaches simply ignore the internal semantic region correlation. As is well known, the correlation reveals the salient information of images, which can further boost the performance of fine-grained image classification. To this end, we propose an Object Decoupling with Graph Correlation network (ODGC) to explore the informative potentials of region correlation. A Responsive Object Location Module (ROLM) is first introduced to obtain the fine-grained object within a bounding box automatically. A Semantic Decoupling Module (SDM) then segments the object into different parts. ODGC learns the representations of these parts by transferring these part features into a Graph Correlation Module (GCM). Consists of these three main modules, ODGC is trained for fine-grained image classification in an end-to-end way. Extensive experiments conducted on CUB-200-2011 demonstrate that the aforementioned modules significantly improve the ODGC, and it achieves a new state-of-the-art performance to 88.2% top-1 accuracy. Besides, we collect a practical business e-commercial dataset, named Ecom-15K. The evaluation on it further validates the applicability of our method in practical scenarios.