  From the autonomous car driving to medical diagnosis, the requirement of the
task of image segmentation is everywhere. Segmentation of an image is one of
the indispensable tasks in computer vision. This task is comparatively
complicated than other vision tasks as it needs low-level spatial information.
Basically, image segmentation can be of two types: semantic segmentation and
instance segmentation. The combined version of these two basic tasks is known
as panoptic segmentation. In the recent era, the success of deep convolutional
neural networks (CNN) has influenced the field of segmentation greatly and gave
us various successful models to date. In this survey, we are going to take a
glance at the evolution of both semantic and instance segmentation work based
on CNN. We have also specified comparative architectural details of some
state-of-the-art models and discuss their training details to present a lucid
understanding of hyper-parameter tuning of those models. We have also drawn a
comparison among the performance of those models on different datasets. Lastly,
we have given a glimpse of some state-of-the-art panoptic segmentation models.
