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Learning Object Representations for Modeling Attention in Real World Scenes

Many models of visual attention exist, but only a few have been shown with real-world scenes. Beuth, F. and F. H. Hamker, 2015, NCNC proposed such a model, which is a succeeding model of Hamker, F. H., 2005, Cerebral Cortex . We show how the object representations in this model (Fig. 1) have been learned in a biological plausible way. To learn the representations, we use the principle of temporal continuity as it has been hypothesized that the primate brain uses temporal continuity for the development of invariant objects representations (Foldiak, P., 1991, Neural Computation). The idea is that on the short time scale of stimuli presentation, the visual input is more likely to originate from the same object, rather than a different one. However, temporal continuity models have been typically used in simple scenes composed of bars, but not in a real-world task.