Learning Deep Representations for Semantic Image Parsing: a Comprehensive Overview

TL;DR

This paper provides a comprehensive overview of deep learning techniques for semantic image parsing, covering various tasks, methods, datasets, and future challenges in the field.

Contribution

It systematically summarizes recent progress, frameworks, and benchmarks in semantic image parsing, highlighting advantages, limitations, and future directions.

Findings

Review of general frameworks for semantic segmentation tasks

Comparison of benchmark datasets and evaluation metrics

Discussion of future trends and challenges

Abstract

Semantic image parsing, which refers to the process of decomposing images into semantic regions and constructing the structure representation of the input, has recently aroused widespread interest in the field of computer vision. The recent application of deep representation learning has driven this field into a new stage of development. In this paper, we summarize three aspects of the progress of research on semantic image parsing, i.e., category-level semantic segmentation, instance-level semantic segmentation, and beyond segmentation. Specifically, we first review the general frameworks for each task and introduce the relevant variants. The advantages and limitations of each method are also discussed. Moreover, we present a comprehensive comparison of different benchmark datasets and evaluation metrics. Finally, we explore the future trends and challenges of semantic image parsing.