# A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains

arXiv: https://arxiv.org/abs/1912.11186 · HTML: https://mlsanity.com/paper/1912.11186/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2019.

## Built on by (most cited first, 30 of 33)

- [Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization](https://mlsanity.com/paper/2205.07839/) (2022)
- [Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning](https://mlsanity.com/paper/2309.01246/) (2023)
- [Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs](https://mlsanity.com/paper/2103.03129/) (2021)
- [Pointly-Supervised Panoptic Segmentation](https://mlsanity.com/paper/2210.13950/) (2022)
- [Weakly-Supervised Semantic Segmentation with Image-Level Labels: from Traditional Models to Foundation Models](https://mlsanity.com/paper/2310.13026/) (2023)
- [Background Activation Suppression for Weakly Supervised Object Localization and Semantic Segmentation](https://mlsanity.com/paper/2309.12943/) (2023)
- [Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer](https://mlsanity.com/paper/2301.02933/) (2023)
- [WeGleNet: A Weakly-Supervised Convolutional Neural Network for the Semantic Segmentation of Gleason Grades in Prostate Histology Images](https://mlsanity.com/paper/2105.10445/) (2021)
- [AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain Tumor](https://mlsanity.com/paper/2306.14505/) (2023)
- [WeakMedSAM: Weakly-Supervised Medical Image Segmentation via SAM with Sub-Class Exploration and Prompt Affinity Mining](https://mlsanity.com/paper/2503.04106/) (2025)
- [A Novel Graph-Theoretic Deep Representation Learning Method for Multi-Label Remote Sensing Image Retrieval](https://mlsanity.com/paper/2106.00506/) (2021)
- [Mixed-UNet: Refined Class Activation Mapping for Weakly-Supervised Semantic Segmentation with Multi-scale Inference](https://mlsanity.com/paper/2205.04227/) (2022)
- [Upsampling DINOv2 features for unsupervised vision tasks and weakly supervised materials segmentation](https://mlsanity.com/paper/2410.19836/) (2024)
- [Weakly Supervised Semantic Segmentation of Remote Sensing Images for Tree Species Classification Based on Explanation Methods](https://mlsanity.com/paper/2201.07495/) (2022)
- [Learning Neural Eigenfunctions for Unsupervised Semantic Segmentation](https://mlsanity.com/paper/2304.02841/) (2023)
- [Rethinking Lightweight Convolutional Neural Networks for Efficient and High-quality Pavement Crack Detection](https://mlsanity.com/paper/2109.05707/) (2021)
- [CheXseg: Combining Expert Annotations with DNN-generated Saliency Maps for X-ray Segmentation](https://mlsanity.com/paper/2102.10484/) (2021)
- [Learning to segment prostate cancer by aggressiveness from scribbles in bi-parametric MRI](https://mlsanity.com/paper/2207.05056/) (2022)
- [ISIM: Iterative Self-Improved Model for Weakly Supervised Segmentation](https://mlsanity.com/paper/2211.12455/) (2022)
- [Superpixel Boundary Correction for Weakly-Supervised Semantic Segmentation on Histopathology Images](https://mlsanity.com/paper/2501.03891/) (2025)
- [Weakly-Supervised Cloud Detection with Fixed-Point GANs](https://mlsanity.com/paper/2111.11879/) (2021)
- [Experimental Large-Scale Jet Flames' Geometrical Features Extraction for Risk Management Using Infrared Images and Deep Learning Segmentation Methods](https://mlsanity.com/paper/2201.07931/) (2022)
- [Weakly Supervised Deep Instance Nuclei Detection using Points Annotation in 3D Cardiovascular Immunofluorescent Images](https://mlsanity.com/paper/2208.00098/) (2022)
- [Weakly supervised segmentation of intracranial aneurysms using a novel 3D focal modulation UNet](https://mlsanity.com/paper/2308.03001/) (2023)
- [A drone-based framework for coral habitat mapping via weakly supervised segmentation](https://mlsanity.com/paper/2508.18958/) (2025)
- [Expectation-Maximization Regularized Deep Learning for Weakly Supervised Tumor Segmentation for Glioblastoma](https://mlsanity.com/paper/2101.08757/) (2021)
- [Semantic Segmentation of Anaemic RBCs Using Multilevel Deep Convolutional Encoder-Decoder Network](https://mlsanity.com/paper/2202.04650/) (2022)
- [Learning with Weak Annotations for Robust Maritime Obstacle Detection](https://mlsanity.com/paper/2206.13263/) (2022)
- [Enhancing Weakly Supervised Semantic Segmentation for Fibrosis via Controllable Image Generation](https://mlsanity.com/paper/2411.03551/) (2024)
- [Opening the black box of deep learning: Validating the statistical association between explainable artificial intelligence (XAI) and clinical domain knowledge in fundus image-based glaucoma diagnosis](https://mlsanity.com/paper/2504.04549/) (2025)

## Abstract (arXiv)

Recently proposed methods for weakly-supervised semantic segmentation have achieved impressive performance in predicting pixel classes despite being trained with only image labels which lack positional information. Because image annotations are cheaper and quicker to generate, weak supervision is more practical than full supervision for training segmentation algorithms. These methods have been predominantly developed to solve the background separation and partial segmentation problems presented by natural scene images and it is unclear whether they can be simply transferred to other domains with different characteristics, such as histopathology and satellite images, and still perform well. This paper evaluates state-of-the-art weakly-supervised semantic segmentation methods on natural scene, histopathology, and satellite image datasets and analyzes how to determine which method is most suitable for a given dataset. Our experiments indicate that histopathology and satellite images present a different set of problems for weakly-supervised semantic segmentation than natural scene images, such as ambiguous boundaries and class co-occurrence. Methods perform well for datasets they were developed on, but tend to perform poorly on other datasets. We present some practical techniques for these methods on unseen datasets and argue that more work is needed for a generalizable approach to weakly-supervised semantic segmentation. Our full code implementation is available on GitHub: https://github.com/lyndonchan/wsss-analysis.