  This paper investigates the role of saliency to improve the classification
accuracy of a Convolutional Neural Network (CNN) for the case when scarce
training data is available. Our approach consists in adding a saliency branch
to an existing CNN architecture which is used to modulate the standard
bottom-up visual features from the original image input, acting as an
attentional mechanism that guides the feature extraction process. The main aim
of the proposed approach is to enable the effective training of a fine-grained
recognition model with limited training samples and to improve the performance
on the task, thereby alleviating the need to annotate large dataset. % The vast
majority of saliency methods are evaluated on their ability to generate
saliency maps, and not on their functionality in a complete vision pipeline.
Our proposed pipeline allows to evaluate saliency methods for the high-level
task of object recognition. We perform extensive experiments on various
fine-grained datasets (Flowers, Birds, Cars, and Dogs) under different
conditions and show that saliency can considerably improve the network's
performance, especially for the case of scarce training data. Furthermore, our
experiments show that saliency methods that obtain improved saliency maps (as
measured by traditional saliency benchmarks) also translate to saliency methods
that yield improved performance gains when applied in an object recognition
pipeline.
