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We demonstrate the use of an extensive deep neural network to localizeinstances of objects in images. The EDNN is naturally able to accuratelyperform multi-class counting using only ground truth count values as labels.Without providing any conceptual information, object annotations, or pixelsegmentation information, the neural network is able to formulate its ownconceptual representation of the items in the image. Using images labelled withonly the counts of the objects present,the structure of the extensive deepneural network can be exploited to perform localization of the objects withinthe visual field. We demonstrate that a trained EDNN can be used to countobjects in images much larger than those on which it was trained. In order todemonstrate our technique, we introduce seven new data sets: five progressivelyharder MNIST digit-counting data sets, and two datasets of 3d-rendered rubberducks in various situations. On most of these datasets, the EDNN achievesgreater than 99% test set accuracy in counting objects.

Weakly-supervised multi-class object localization using only object counts as labels