  Most of the saliency methods are evaluated on their ability to generate
saliency maps, and not on their functionality in a complete vision pipeline,
like for instance, image classification. In the current paper, we propose an
approach which does not require explicit saliency maps to improve image
classification, but they are learned implicitely, during the training of an
end-to-end image classification task. We show that our approach obtains similar
results as the case when the saliency maps are provided explicitely. Combining
RGB data with saliency maps represents a significant advantage for object
recognition, especially for the case when training data is limited. We validate
our method on several datasets for fine-grained classification tasks (Flowers,
Birds and Cars). In addition, we show that our saliency estimation method,
which is trained without any saliency groundtruth data, obtains competitive
results on real image saliency benchmark (Toronto), and outperforms deep
saliency models with synthetic images (SID4VAM).
