@Article{CarolaFigueroaFlores2021,
author="Carola Figueroa Flores
and David Berga
and Joost Van de Weijer
and Bogdan Raducanu",
title="Saliency for free: Saliency prediction as a side-effect of object recognition",
journal="Pattern Recognition Letters",
year="2021",
volume="150",
pages="1--7",
optkeywords="Saliency maps",
optkeywords="Unsupervised learning",
optkeywords="Object recognition",
abstract="Saliency is the perceptual capacity of our visual system to focus our attention (i.e. gaze) on relevant objects instead of the background. So far, computational methods for saliency estimation required the explicit generation of a saliency map, process which is usually achieved via eyetracking experiments on still images. This is a tedious process that needs to be repeated for each new dataset. In the current paper, we demonstrate that is possible to automatically generate saliency maps without ground-truth. In our approach, saliency maps are learned as a side effect of object recognition. Extensive experiments carried out on both real and synthetic datasets demonstrated that our approach is able to generate accurate saliency maps, achieving competitive results when compared with supervised methods.",
optnote="LAMP; 600.147; 600.120",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3559), last updated on Fri, 07 Jan 2022 12:26:35 +0100",
opturl="https://doi.org/10.1016/j.patrec.2021.05.015",
file=":http://refbase.cvc.uab.es/files/FBW2021.pdf:PDF"
}