A Deeper Look at Saliency: Feature Contrast, Semantics, and Beyond

Neil D. B. Bruce, Christopher Catton, Sasa Janjic; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 516-524

Abstract


In this paper we consider the problem of visual saliency modeling, including both human gaze prediction and salient object segmentation. The overarching goal of the paper is to identify high level considerations relevant to deriving more sophisticated visual saliency models. A deep learning model based on fully convolutional networks (FCNs) is presented, which shows very favorable performance across a wide variety of benchmarks relative to existing proposals. We also demonstrate that the manner in which training data is selected, and ground truth treated is critical to resulting model behaviour. Recent efforts have explored the relationship between human gaze and salient objects, and we also examine this point further in the context of FCNs. Close examination of the proposed and alternative models serves as a vehicle for identifying problems important to developing more comprehensive models going forward.

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[bibtex]
@InProceedings{Bruce_2016_CVPR,
author = {Bruce, Neil D. B. and Catton, Christopher and Janjic, Sasa},
title = {A Deeper Look at Saliency: Feature Contrast, Semantics, and Beyond},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}