  Recent experiments in computer vision demonstrate texture bias as the primary
reason for supreme results in models employing Convolutional Neural Networks
(CNNs), conflicting with early works claiming that these networks identify
objects using shape. It is believed that the cost function forces the CNN to
take a greedy approach and develop a proclivity for local information like
texture to increase accuracy, thus failing to explore any global statistics. We
propose CognitiveCNN, a new intuitive architecture, inspired from feature
integration theory in psychology to utilise human interpretable feature like
shape, texture, edges etc. to reconstruct, and classify the image. We define
novel metrics to quantify the "relevance" of "abstract information" present in
these modalities using attention maps. We further introduce a regularisation
method which ensures that each modality like shape, texture etc. gets
proportionate influence in a given task, as it does for reconstruction; and
perform experiments to show the resulting boost in accuracy and robustness,
besides imparting explainability to these CNNs for achieving superior
performance in object recognition.
