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SAM: Pushing the Limits of Saliency Prediction Models

Abstract: The prediction of human eye fixations has been recently gaining a lot of attention thanks to the improvements shown by deep architectures. In our work, we go beyond classical feed-forward networks to predict saliency maps and propose a Saliency Attentive Model which incorporates neural attention mechanisms to iteratively refine predictions. Experiments demonstrate that the proposed strategy overcomes by a considerable margin the state of the art on the largest dataset available for saliency prediction. Here, we provide experimental results on other popular saliency datasets to confirm the effectiveness and the generalization capabilities of our model, which enable us to reach the state of the art on all considered datasets.


Cornia, Marcella; Baraldi, Lorenzo; Serra, Giuseppe; Cucchiara, Rita "SAM: Pushing the Limits of Saliency Prediction Models" 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, pp. 1971 -1973 , June 18-22 2018, 2018 DOI: 10.1109/CVPRW.2018.00250

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