Dual attention guided multi-scale CNN for fine-grained image classification

For the classification of fine-grained images, the subtle differences among the subclasses of the main category must be distinguished. Intuitively, the key to realizing the fine-grained image categorization lies in locating and identifying the detailed differences in the local regions and capturing their feature representations. In this paper, we propose utilizing an attention module combined with a multi-scale latent representation network to locate the discriminative spatial regions, and then learn an accurate attention map to assist the category decision. Furthermore, an attention module is also employed to determine the channel weights of the distinct scale feature maps before the final step. Extensive experiments demonstrate that our model obtains a competitive performance against state-of-the-art baselines on two benchmark datasets, the attention validation experiments further reveal the ability of the model in choosing the proper channel features for low-quality image categorization.