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Locating discriminative parts plays a key role in fine-grained visualclassification due to the high similarities between different objects. Recentworks based on convolutional neural networks utilize the feature maps takenfrom the last convolutional layer to mine discriminative regions. However, thelast convolutional layer tends to focus on the whole object due to the largereceptive field, which leads to a reduced ability to spot the differences. Toaddress this issue, we propose a novel Granularity-Aware Convolutional NeuralNetwork (GA-CNN) that progressively explores discriminative features.Specifically, GA-CNN utilizes the differences of the receptive fields atdifferent layers to learn multi-granularity features, and it exploits largergranularity information based on the smaller granularity information found atthe previous stages. To further boost the performance, we introduce anobject-attentive module that can effectively localize the object given a rawimage. GA-CNN does not need bounding boxes/part annotations and can be trainedend-to-end. Extensive experimental results show that our approach achievesstate-of-the-art performances on three benchmark datasets.

Learning Granularity-Aware Convolutional Neural Network for Fine-Grained Visual Classification