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In this paper, a novel approach to visual salience detection via NeuralResponse Divergence (NeRD) is proposed, where synaptic portions of deep neuralnetworks, previously trained for complex object recognition, are leveraged tocompute low level cues that can be used to compute image regiondistinctiveness. Based on this concept , an efficient visual salience detectionframework is proposed using deep convolutional StochasticNets. Experimentalresults using CSSD and MSRA10k natural image datasets show that the proposedNeRD approach can achieve improved performance when compared tostate-of-the-art image saliency approaches, while the attaining lowcomputational complexity necessary for near-real-time computer visionapplications.

NeRD: a Neural Response Divergence Approach to Visual Salience Detection