'Institute of Electrical and Electronics Engineers (IEEE)'
Abstract
A key problem in learning multiple objects from unlabeled
images is that it is a priori impossible to tell which
part of the image corresponds to each individual object,
and which part is irrelevant clutter which is not associated
to the objects. We investigate empirically to what extent
pure bottom-up attention can extract useful information
about the location, size and shape of objects from images
and demonstrate how this information can be utilized
to enable unsupervised learning of objects from unlabeled
images. Our experiments demonstrate that the proposed approach to using bottom-up attention is indeed useful for a
variety of applications