  Recent advances in fine-grained recognition utilize attention maps to
localize objects of interest. Although there are many ways to generate
attention maps, most of them rely on sophisticated loss functions or complex
training processes. In this work, we propose a simple and straightforward
attention generation model based on the output activations of classifiers. The
advantage of our model is that it can be easily trained with image level labels
and softmax loss functions. More specifically, multiple linear local
classifiers are firstly adopted to perform fine-grained classification at each
location of high level CNN feature maps. The attention map is generated by
aggregating and max-pooling the output activations. Then the attention map
serves as a surrogate target object mask to train those local classifiers,
similar to training models for semantic segmentation. Our model achieves
state-of-the-art results on three heavily benchmarked datasets, i.e. 87.9% on
CUB-200-2011 dataset, 94.1% on Stanford Cars dataset and 92.1% on FGVC-Aircraft
dataset, demonstrating its effectiveness on fine-grained recognition tasks.
