  Humans process visual scenes selectively and sequentially using attention.
Central to models of human visual attention is the saliency map. We propose a
hierarchical visual architecture that operates on a saliency map and uses a
novel attention mechanism to sequentially focus on salient regions and take
additional glimpses within those regions. The architecture is motivated by
human visual attention, and is used for multi-label image classification on a
novel multiset task, demonstrating that it achieves high precision and recall
while localizing objects with its attention. Unlike conventional multi-label
image classification models, the model supports multiset prediction due to a
reinforcement-learning based training process that allows for arbitrary label
permutation and multiple instances per label.

Computer Vision and Pattern Recognition;Artificial Intelligence