  Deep learning architectures are an extremely powerful tool for recognizing
and classifying images. However, they require supervised learning and normally
work on vectors the size of image pixels and produce the best results when
trained on millions of object images. To help mitigate these issues, we propose
the fusion of bottom-up saliency and top-down attention employing only
unsupervised learning techniques, which helps the object recognition module to
focus on relevant data and learn important features that can later be
fine-tuned for a specific task. In addition, by utilizing only relevant
portions of the data, the training speed can be greatly improved. We test the
performance of the proposed Gamma saliency technique on the Toronto and CAT2000
databases, and the foveated vision in the Street View House Numbers (SVHN)
database. The results in foveated vision show that Gamma saliency is comparable
to the best and computationally faster. The results in SVHN show that our
unsupervised cognitive architecture is comparable to fully supervised methods
and that the Gamma saliency also improves CNN performance if desired. We also
develop a topdown attention mechanism based on the Gamma saliency applied to
the top layer of CNNs to improve scene understanding in multi-object images or
images with strong background clutter. When we compare the results with human
observers in an image dataset of animals occluded in natural scenes, we show
that topdown attention is capable of disambiguating object from background and
improves system performance beyond the level of human observers.
