Computational Neuroscience Commons^™

Predicting Fixations From Deep And Low-Level Features, Matthias Kümmerer, Thomas S.A. Wallis, Leon A. Gatys, Matthias Bethge

MODVIS Workshop

Learning what properties of an image are associated with human gaze placement is important both for understanding how biological systems explore the environment and for computer vision applications. Recent advances in deep learning for the first time enable us to explain a significant portion of the information expressed in the spatial fixation structure. Our saliency model DeepGaze II uses the VGG network (trained on object recognition in the ImageNet challenge) to convert an image into a high-dimensional feature space which is then readout by a second very simple network to yield a density prediction. DeepGaze II is right now the …