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.
Subsequent research effort has been directed at finding neural correlates of the saliency map and priority map. Some proposed areas for salience computation include the superficial layers of the superior colliculus (sSC) and inferior sections of the pulvinar (PI), and for priority map computation include the frontal eye field (FEF) and deeper layers of the superior colliculus (dSC) @cite_5 . Here, we need to only assume existence of the maps as conceptual mechanisms involved in influencing visual attention and refer the reader to @cite_5 for a recent review.
@cite_5
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Human vision system actively seeks salient regions and movements in video sequences to reduce the search effort. Modeling computational visual saliency map provides important information for semantic understanding in many real world applications. In this paper, we propose a novel video saliency detection model for detecting the attended regions that correspond to both interesting objects and dominant motions in video sequences. In spatial saliency map, we inherit the classical bottom-up spatial saliency map. In temporal saliency map, a novel optical flow model is proposed based on the dynamic consistency of motion. The spatial and the temporal saliency maps are constructed and further fused together to create a novel attention model. The proposed attention model is evaluated on three video datasets. Empirical validations demonstrate the salient regions detected by our dynamic consistent saliency map highlight the interesting objects effectively and efficiency. More importantly, the automatically video attended regions detected by proposed attention model are consistent with the ground truth saliency maps of eye movement data.