# Active Learning with Context Sampling and One-vs-Rest Entropy for Semantic Segmentation

arXiv: https://arxiv.org/abs/2412.06470 · HTML: https://mlsanity.com/paper/2412.06470/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 39 references that are in the map.
- First posted on arXiv 9 Dec 2024.

## Builds on (most cited first, 29 of 29)

- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Segmenter: Transformer for Semantic Segmentation](https://mlsanity.com/paper/2105.05633/) (2021)
- [What's the Point: Semantic Segmentation with Point Supervision](https://mlsanity.com/paper/1506.02106/) (2015)
- [Variational Adversarial Active Learning](https://mlsanity.com/paper/1904.00370/) (2019)
- [Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation](https://mlsanity.com/paper/1706.04737/) (2017)
- [2018 Robotic Scene Segmentation Challenge](https://mlsanity.com/paper/2001.11190/) (2020)
- [ViewAL: Active Learning with Viewpoint Entropy for Semantic Segmentation](https://mlsanity.com/paper/1911.11789/) (2019)
- [SEEDS: Superpixels Extracted via Energy-Driven Sampling](https://mlsanity.com/paper/1309.3848/) (2013)
- [Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2111.12940/) (2021)
- [Reinforced active learning for image segmentation](https://mlsanity.com/paper/2002.06583/) (2020)
- [CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation](https://mlsanity.com/paper/1810.09726/) (2018)
- [DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image Segmentation](https://mlsanity.com/paper/2101.09057/) (2021)
- [MetaBox+: A new Region Based Active Learning Method for Semantic Segmentation using Priority Maps](https://mlsanity.com/paper/2010.01884/) (2020)
- [Importance of Self-Consistency in Active Learning for Semantic Segmentation](https://mlsanity.com/paper/2008.01860/) (2020)
- [Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling](https://mlsanity.com/paper/2006.14984/) (2020)
- [Algorithm Selection for Deep Active Learning with Imbalanced Datasets](https://mlsanity.com/paper/2302.07317/) (2023)
- [DIAL: Deep Interactive and Active Learning for Semantic Segmentation in Remote Sensing](https://mlsanity.com/paper/2201.01047/) (2022)
- [Class-Balanced Active Learning for Image Classification](https://mlsanity.com/paper/2110.04543/) (2021)
- [Adaptive Superpixel for Active Learning in Semantic Segmentation](https://mlsanity.com/paper/2303.16817/) (2023)
- [Minority Class Oriented Active Learning for Imbalanced Datasets](https://mlsanity.com/paper/2202.00390/) (2022)
- [CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification](https://mlsanity.com/paper/2210.01520/) (2022)
- [Active Learning for Semantic Segmentation with Multi-class Label Query](https://mlsanity.com/paper/2309.09319/) (2023)
- [Active Class Incremental Learning for Imbalanced Datasets](https://mlsanity.com/paper/2008.10968/) (2020)
- [Reducing Annotating Load: Active Learning with Synthetic Images in Surgical Instrument Segmentation](https://mlsanity.com/paper/2108.03534/) (2021)
- [MEAL: Manifold Embedding-based Active Learning](https://mlsanity.com/paper/2106.11858/) (2021)
- [Active learning for imbalanced data under cold start](https://mlsanity.com/paper/2107.07724/) (2021)
- [Correlation-aware active learning for surgery video segmentation](https://mlsanity.com/paper/2311.08811/) (2023)
- [Weakly-Supervised Semantic Segmentation by Learning Label Uncertainty](https://mlsanity.com/paper/2110.05926/) (2021)

## Built on by (most cited first, 2 of 2)

- [A2LC: Active and Automated Label Correction for Semantic Segmentation](https://mlsanity.com/paper/2506.11599/) (2025)
- [Closing the Expression Gap in LLM Instructions via Socratic Questioning](https://mlsanity.com/paper/2510.27410/) (2025)

## Abstract (arXiv)

Multi-class semantic segmentation remains a cornerstone challenge in computer vision. Yet, dataset creation remains excessively demanding in time and effort, especially for specialized domains. Active Learning (AL) mitigates this challenge by selecting data points for annotation strategically. However, existing patch-based AL methods often overlook boundary pixels critical information, essential for accurate segmentation. We present OREAL, a novel patch-based AL method designed for multi-class semantic segmentation. OREAL enhances boundary detection by employing maximum aggregation of pixel-wise uncertainty scores. Additionally, we introduce one-vs-rest entropy, a novel uncertainty score function that computes class-wise uncertainties while achieving implicit class balancing during dataset creation. Comprehensive experiments across diverse datasets and model architectures validate our hypothesis.