  In the past decade, deep convolutional neural networks have achieved
significant success in image classification and ranking and have therefore
found numerous applications in multimedia content retrieval. Still, these
models suffer from performance degradation when neural networks are tested on
out-of-distribution scenarios or on data originating from previously unseen
data Domains. In the present work, we focus on this problem of Domain
Generalization and propose an alternative neural network architecture for
robust, out-of-distribution image classification. We attempt to produce a model
that focuses on the causal features of the depicted class for robust image
classification in the Domain Generalization setting. To achieve this, we
propose attending to multiple-levels of information throughout a Convolutional
Neural Network and leveraging the most important attributes of an image by
employing trainable attention mechanisms. To validate our method, we evaluate
our model on four widely accepted Domain Generalization benchmarks, on which
our model is able to surpass previously reported baselines in three out of four
datasets and achieve the second best score in the fourth one.
