  Semantic image segmentation is one of fastest growing areas in computer
vision with a variety of applications. In many areas, such as robotics and
autonomous vehicles, semantic image segmentation is crucial, since it provides
the necessary context for actions to be taken based on a scene understanding at
the pixel level. Moreover, the success of medical diagnosis and treatment
relies on the extremely accurate understanding of the data under consideration
and semantic image segmentation is one of the important tools in many cases.
Recent developments in deep learning have provided a host of tools to tackle
this problem efficiently and with increased accuracy. This work provides a
comprehensive analysis of state-of-the-art deep learning architectures in image
segmentation and, more importantly, an extensive list of techniques to achieve
fast inference and computational efficiency. The origins of these techniques as
well as their strengths and trade-offs are discussed with an in-depth analysis
of their impact in the area. The best-performing architectures are summarized
with a list of methods used to achieve these state-of-the-art results.
