  Fine-grained image recognition is central to many multimedia tasks such as
search, retrieval and captioning. Unfortunately, these tasks are still
challenging since the appearance of samples of the same class can be more
different than those from different classes. Attention has been typically
implemented in neural networks by selecting the most informative regions of the
image that improve classification. In contrast, in this paper, attention is not
applied at the image level but to the convolutional feature activations. In
essence, with our approach, the neural model learns to attend to lower-level
feature activations without requiring part annotations and uses those
activations to update and rectify the output likelihood distribution. The
proposed mechanism is modular, architecture-independent and efficient in terms
of both parameters and computation required. Experiments demonstrate that
well-known networks such as Wide Residual Networks and ResNeXt, when augmented
with our approach, systematically improve their classification accuracy and
become more robust to changes in deformation and pose and to the presence of
clutter. As a result, our proposal reaches state-of-the-art classification
accuracies in CIFAR-10, the Adience gender recognition task, Stanford Dogs, and
UEC-Food100 while obtaining competitive performance in ImageNet, CIFAR-100,
CUB200 Birds, and Stanford Cars. In addition, we analyze the different
components of our model, showing that the proposed attention modules succeed in
finding the most discriminative regions of the image. Finally, as a proof of
concept, we demonstrate that with only local predictions, an augmented neural
network can successfully classify an image before reaching any fully connected
layer, thus reducing the computational amount up to 10%.
