# Learning Deep Representations for Semantic Image Parsing: a Comprehensive Overview

Source: [Learning Deep Representations for Semantic Image Parsing: a Comprehensive Overview](<https://www.sota2.com/research/paper/68fa64e21e209131cea0715c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:13:58.604Z

Authors: Lili Huang, Jiefeng Peng, Ruimao Zhang, Guanbin Li, Liang Lin

Published: 2018-10-10T05:29:47.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1810.04377>)

[PDF](<https://arxiv.org/pdf/1810.04377.pdf>)