ML approaches to saliency make ---- bitte auswählen ---- ( minimal ; maximal ) ---- bitte auswählen ----( posterior ; prior ) assumptions regarding the computational architecture of the visual system. Typically a ---- bitte auswählen ---- ( rather generic ; carefully tuned ; highly specific ) ML algorithm is trained on a ---- bitte auswählen ---- ( small ; large ) dataset and prediction of the fixation locations is ---- bitte auswählen ---- ( optimised ; randomised ; treated as nuisance variable) . The currently best performing visual saliency models are based on ---- bitte auswählen ---- deep neural networks spatial point processes support vector machines Gaussian processes Bayesian analysis .
ML approaches to saliency make minimal prior assumptions regarding the computational architecture of the visual system. Typically a rather generic ML algorithm is trained on a large dataset and prediction of the fixation locations is optimised . The currently best performing visual saliency models are based on deep neural networks .
In the beginning, the mid 1990s until before DNNs became prominent as models of visual saliency, saliency was thought to be a ---- bitte auswählen ---- ( bottom-up ; top-down ) ---- bitte auswählen ---- ( stimulus-driven ; salient ; predictive ) signal. The main idea of early saliency models is that signals ---- bitte auswählen ---- ( select ; compete for ; align visual ; synchronise the ) representation(s).
In the beginning, the mid 1990s until before DNNs became prominent as models of visual saliency, saliency was thought to be a bottom-upstimulus-driven signal. The main idea of early saliency models is that signals compete for representation(s).
What is visual saliency?
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