Dirk B. Walther

TL;DR: It is demonstrated that the suggested model can enable a model of object recognition in cortex to expand from recognizing individual objects in isolation to sequentially recognizing all objects in a more complex scene.

TL;DR: Empirically to what extent pure bottom-up attention can extract useful information about the location, size and shape of objects from images and how this information can be utilized to enable unsupervised learning of Objects from unlabeled images is investigated.

TL;DR: In this paper, the authors used functional magnetic resonance imaging (fMRI) and distributed pattern analysis to ask what regions of the brain can differentiate natural scene categories (such as forests vs mountains vs beaches) and found that area V1, the parahippocampal place area (PPA), retrosplenial cortex (RSC), and lateral occipital complex (LOC) all contain information that distinguishes among natural scene classes.

TL;DR: A combined model for spatial attention and object recognition in which the recognition system monitors the entire visual field, but attentional modulation by as little as 20% at a high level is sufficient to recognize multiple objects.

TL;DR: The proposed method for the selection of salient regions likely to contain objects, based on bottom-up visual attention, can enable one-shot learning of multiple objects from complex scenes, and can strongly improve learning and recognition performance in the presence of large amounts of clutter.