Abstract
We propose a novel attention mechanism to enhance Convolutional Neural
Networks for fine-grained recognition. It learns to attend to lower-level
feature activations without requiring part annotations and uses these
activations to update and rectify the output likelihood distribution. In
contrast to other approaches, the proposed mechanism is modular,
architecture-independent and efficient both in terms of parameters and
computation required. Experiments show that networks augmented with our
approach systematically improve their classification accuracy and become more
robust to clutter. As a result, Wide Residual Networks augmented with our
proposal surpasses the state of the art classification accuracies in CIFAR-10,
the Adience gender recognition task, Stanford dogs, and UEC Food-100.
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