  Instance segmentation is an advanced form of image segmentation which, beyond
traditional segmentation, requires identifying individual instances of
repeating objects in a scene. Mask R-CNN is the most common architecture for
instance segmentation, and improvements to this architecture include steps such
as benefiting from bounding box refinements, adding semantics, or backbone
enhancements. In all the proposed variations to date, the problem of competing
kernels (each class aims to maximize its own accuracy) persists when models try
to synchronously learn numerous classes. In this paper, we propose mitigating
this problem by replacing mask prediction with a Switch-Split block that
processes refined ROIs, classifies them, and assigns them to specialized mask
predictors. We name the method MaskUno and test it on various models from the
literature, which are then trained on multiple classes using the benchmark COCO
dataset. An increase in the mean Average Precision (mAP) of 2.03% was observed
for the high-performing DetectoRS when trained on 80 classes. MaskUno proved to
enhance the mAP of instance segmentation models regardless of the number and
typ
