LAUSR

In fine-grained visual categorization (FGVC), most part-based frameworks do not work effectively in some extremely challenging scenarios such as partial occlusion. This limitation is due to the heavy disorder of local features extracted from such occluded targets. To address this issue, we propose a global information-assisted network (GIAN), where auxiliary global information can search the useful elements of local information and integrate with them for an efficient unified feature representation. In particular, in order to acquire the global information, we design a global attention-concentrated convolutional neural network (GAC-CNN) by extending a convolutional neural network with a nonlocal GCN module. Then, the unified feature representation is produced by two strategies. On the one hand, a global–local aggregation strategy is developed to selectively integrate global features with local features through consistency evaluation and reweighting method. On the other hand, an alternative knowledge distillation strategy is developed to help generate more powerful global and local features. Two strategies collaboratively make the unified features more robust and more discriminative than traditional part-based features. Experimental results show that the proposed GIAN can achieve accuracies of 92.8%, 93.8%, and 95.7% on CUB-200-2011, FGVC Aircraft, and Stanford Cars, respectively.