  In machine learning and other fields, suggesting a good solution to a problem
is usually a harder task than evaluating the quality of such a solution. This
asymmetry is the basis for a large number of selection oriented methods that
use a generator system to guess a set of solutions and an evaluator system to
rank and select the best solutions. This work examines the use of this approach
to the problem of panoptic image segmentation and class agnostic parts
segmentation. The generator/evaluator approach for this case consists of two
independent convolutional neural nets: a generator net that suggests variety
segments corresponding to objects, stuff and parts regions in the image, and an
evaluator net that chooses the best segments to be merged into the segmentation
map. The result is a trial and error evolutionary approach in which a generator
that guesses segments with low average accuracy, but with wide variability, can
still produce good results when coupled with an accurate evaluator. The
generator consists of a Pointer net that receives an image and a point in the
image, and predicts the region of the segment containing the point. Generating
and evaluating each segment separately is essential in this case since it
demands exponentially fewer guesses compared to a system that guesses and
evaluates the full segmentation map in each try. The classification of the
selected segments is done by an independent region-specific classification net.
This allows the segmentation to be class agnostic and hence, capable of
segmenting unfamiliar categories that were not part of the training set. The
method was examined on the COCO Panoptic segmentation benchmark and gave
results comparable to those of the basic semantic segmentation and Mask-RCNN
methods. In addition, the system was used for the task of splitting objects of
unseen classes (that did not appear in the training set) into parts.
