ALMA: Adjustable Location and Multi-Angle Attention for Fine-Grained Visual Classification

Fine-grained visual classification (FGVC) is a challenging but realistic problem that recognizes objects from common categories with subtle differences. Most previous work focused on identifying more regional features while neglecting the fact that these regions still contain a large amount of secondary information. To alleviate the interference of the secondary information, in this paper, we propose a novel Adjustable Location and Multi-angle Attention (ALMA) network to solve the FGVC problem. ALMA consists of two branches, i.e. the adjustable location module and the multi-angle attention module. Specifically, in the adjustable localization module, we first locate the interested area of the object and obtain the adjusted cropped area by adjusting the interested area through the background masking. Then, the adjusted regions will be gathered to locate objects with better prediction performance. Furthermore, we design the multi-angle attention module to gradually maximize the difference between the original attention map and the randomly selected attention map. Consequently, the model can focus on the main information which represents the entire object. To evaluate the effectiveness of the proposed model, we conduct extensive experiments on three public fine-grained benchmark datasets. Experimental results demonstrate that the proposed ALMA model has significant superiority over other FGVC methods.