A Brief Perspective on Deep Learning Approaches for 2D Semantic Segmentation

Semantic segmentation is a vast field with many contributions, which can be difficult to organize and comprehend due to the amount of research available. Advancements in technology and processing power over the past decade have led to a significant increase in the number of developed models and architectures. This paper provides a brief perspective on 2D segmentation by summarizing the mechanisms of various neural network models and the tools and datasets used for their training, testing, and evaluation. Additionally, this paper discusses methods for identifying new architectures, such as Neural Architecture Search, and explores the emerging research field of continuous learning, which aims to develop models capable of learning continuously from new data.