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

Saliency-based Sequential Image Attention with Multiset Prediction