# Recent progress in semantic image segmentation

arXiv: https://arxiv.org/abs/1809.10198 · HTML: https://mlsanity.com/paper/1809.10198/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2018.

## Built on by (most cited first, 30 of 39)

- [Image Segmentation in Foundation Model Era: A Survey](https://mlsanity.com/paper/2408.12957/) (2024)
- [A survey of the Vision Transformers and their CNN-Transformer based Variants](https://mlsanity.com/paper/2305.09880/) (2023)
- [Image Data Augmentation Approaches: A Comprehensive Survey and Future directions](https://mlsanity.com/paper/2301.02830/) (2023)
- [A Recent Survey of Vision Transformers for Medical Image Segmentation](https://mlsanity.com/paper/2312.00634/) (2023)
- [Modality specific U-Net variants for biomedical image segmentation: A survey](https://mlsanity.com/paper/2107.04537/) (2021)
- [Membership Inference Attacks are Easier on Difficult Problems](https://mlsanity.com/paper/2102.07762/) (2021)
- [Hybrid quantum convolutional neural networks model for COVID-19 prediction using chest X-Ray images](https://mlsanity.com/paper/2102.06535/) (2021)
- [Image and Model Transformation with Secret Key for Vision Transformer](https://mlsanity.com/paper/2207.05366/) (2022)
- [RS-vHeat: Heat Conduction Guided Efficient Remote Sensing Foundation Model](https://mlsanity.com/paper/2411.17984/) (2024)
- [When Differential Privacy Meets Interpretability: A Case Study](https://mlsanity.com/paper/2106.13203/) (2021)
- [Mixture Domain Adaptation to Improve Semantic Segmentation in Real-World Surveillance](https://mlsanity.com/paper/2211.10119/) (2022)
- [MEAL: Manifold Embedding-based Active Learning](https://mlsanity.com/paper/2106.11858/) (2021)
- [Access Control Using Spatially Invariant Permutation of Feature Maps for Semantic Segmentation Models](https://mlsanity.com/paper/2109.01332/) (2021)
- [A Saliency-Guided Street View Image Inpainting Framework for Efficient Last-Meters Wayfinding](https://mlsanity.com/paper/2205.06934/) (2022)
- [Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images](https://mlsanity.com/paper/2211.01885/) (2022)
- [MudrockNet: Semantic Segmentation of Mudrock SEM Images through Deep Learning](https://mlsanity.com/paper/2102.03393/) (2021)
- [Access Control of Semantic Segmentation Models Using Encrypted Feature Maps](https://mlsanity.com/paper/2206.05422/) (2022)
- [Benchmarking Vision-Language and Multimodal Large Language Models in Zero-shot and Few-shot Scenarios: A study on Christian Iconography](https://mlsanity.com/paper/2509.18839/) (2025)
- [UCNN: A Convolutional Strategy on Unstructured Mesh](https://mlsanity.com/paper/2101.05207/) (2021)
- [The SAM2-to-SAM3 Gap in the Segment Anything Model Family: Why Prompt-Based Expertise Fails in Concept-Driven Image Segmentation](https://mlsanity.com/paper/2512.06032/) (2025)
- [Access Control of Object Detection Models Using Encrypted Feature Maps](https://mlsanity.com/paper/2202.00265/) (2022)
- [FusionVision: A comprehensive approach of 3D object reconstruction and segmentation from RGB-D cameras using YOLO and fast segment anything](https://mlsanity.com/paper/2403.00175/) (2024)
- [MFA-Net: Multi-Scale feature fusion attention network for liver tumor segmentation](https://mlsanity.com/paper/2405.04064/) (2024)
- [CS3: Cascade SAM for Sperm Segmentation](https://mlsanity.com/paper/2407.03772/) (2024)
- [A Pressure Ulcer Care System For Remote Medical Assistance: Residual U-Net with an Attention Model Based for Wound Area Segmentation](https://mlsanity.com/paper/2101.09433/) (2021)
- [Automatic Liver Segmentation from CT Images Using Deep Learning Algorithms: A Comparative Study](https://mlsanity.com/paper/2101.09987/) (2021)
- [Phase Space Reconstruction Network for Lane Intrusion Action Recognition](https://mlsanity.com/paper/2102.11149/) (2021)
- [High-Resolution Segmentation of Tooth Root Fuzzy Edge Based on Polynomial Curve Fitting with Landmark Detection](https://mlsanity.com/paper/2103.04258/) (2021)
- [Design and Development of Autonomous Delivery Robot](https://mlsanity.com/paper/2103.09229/) (2021)
- [Self Semi Supervised Neural Architecture Search for Semantic Segmentation](https://mlsanity.com/paper/2201.12646/) (2022)

## Abstract (arXiv)

Semantic image segmentation, which becomes one of the key applications in image processing and computer vision domain, has been used in multiple domains such as medical area and intelligent transportation. Lots of benchmark datasets are released for researchers to verify their algorithms. Semantic segmentation has been studied for many years. Since the emergence of Deep Neural Network (DNN), segmentation has made a tremendous progress. In this paper, we divide semantic image segmentation methods into two categories: traditional and recent DNN method. Firstly, we briefly summarize the traditional method as well as datasets released for segmentation, then we comprehensively investigate recent methods based on DNN which are described in the eight aspects: fully convolutional network, upsample ways, FCN joint with CRF methods, dilated convolution approaches, progresses in backbone network, pyramid methods, Multi-level feature and multi-stage method, supervised, weakly-supervised and unsupervised methods. Finally, a conclusion in this area is drawn.