mlsanityData to 14 Oct 2026

CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation

arXiv 1810.09726 ↗First posted 23 Oct 2018cs.CVcited by 49

Not on the map. Why?

Abstract (arXiv)

State of the art methods for semantic image segmentation are trained in a supervised fashion using a large corpus of fully labeled training images. However, gathering such a corpus is expensive, due to human annotation effort, in contrast to gathering unlabeled data. We propose an active learning-based strategy, called CEREALS, in which a human only has to hand-label a few, automatically selected, regions within an unlabeled image corpus. This minimizes human annotation effort while maximizing the performance of a semantic image segmentation method. The automatic selection procedure is achieved by: a) using a suitable information measure combined with an estimate about human annotation effort, which is inferred from a learned cost model, and b) exploiting the spatial coherency of an image. The performance of CEREALS is demonstrated on Cityscapes, where we are able to reduce the annotation effort to 17%, while keeping 95% of the mean Intersection over Union (mIoU) of a model that was trained with the fully annotated training set of Cityscapes.

Key facts. Cited by 49 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Oct 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 10; 2022: 14; 2023: 8; 2024: 7; 2025: 3; 2026: 7.

49 papers.

Sort:

By area:

  1. 2021Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safetycited by 35
  2. 2023A comprehensive survey on deep active learning in medical image analysiscited by 35
  3. 2021All you need are a few pixels: semantic segmentation with PixelPickcited by 31
  4. 2021Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimationcited by 25
  5. 2024ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentationcited by 25
  6. 2022From colouring-in to pointillism: revisiting semantic segmentation supervisioncited by 22
  7. 2021Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approachcited by 17
  8. 2022Active Learning for Point Cloud Semantic Segmentation via Spatial-Structural Diversity Reasoningcited by 16
  9. 2022Semantic Segmentation with Active Semi-Supervised Learningcited by 16
  10. 2022Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous Drivingcited by 16
  11. 2022LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentationcited by 16
  12. 2022Optical Flow Training under Limited Label Budget via Active Learningcited by 14
37 more
  1. 2022Deep Active Learning with Noise Stabilitycited by 14
  2. 2023Adaptive Superpixel for Active Learning in Semantic Segmentationcited by 14
  3. 2023Best Practices in Active Learning for Semantic Segmentationcited by 10
  4. 2021Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite Imagescited by 9
  5. 2023Active Learning for Semantic Segmentation with Multi-class Label Querycited by 9
  6. 2022Deep Active Learning for Computer Vision: Past and Futurecited by 7
  7. 2024Active Label Correction for Semantic Segmentation with Foundation Modelscited by 7
  8. 2021CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentationcited by 6
  9. 2021MEAL: Manifold Embedding-based Active Learningcited by 5
  10. 2022D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic Segmentationcited by 5
  11. 2024Bayesian Active Learning for Semantic Segmentationcited by 5
  12. 2023Correlation-aware active learning for surgery video segmentationcited by 4
  13. 2024Edge-guided and Class-balanced Active Learning for Semantic Segmentation of Aerial Imagescited by 4
  14. 2021Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-Trainingcited by 3
  15. 2023Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentationcited by 3
  16. 2023EdgeAL: An Edge Estimation Based Active Learning Approach for OCT Segmentationcited by 3
  17. 2025Learn 3D VQA Better with Active Selection and Reannotationcited by 3
  18. 2021Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenescited by 2
  19. 2022An Active and Contrastive Learning Framework for Fine-Grained Off-Road Semantic Segmentationcited by 2
  20. 2022Labeling Where Adapting Fails: Cross-Domain Semantic Segmentation with Point Supervision via Active Selectioncited by 2
  21. 2022Pixel is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detectioncited by 2
  22. 2024Active Learning with Context Sampling and One-vs-Rest Entropy for Semantic Segmentationcited by 2
  23. 2022Can I see an Example? Active Learning the Long Tail of Attributes and Relationscited by 1
  24. 2024Leveraging image captions for selective whole slide image annotationcited by 1
  25. 2025Exploring Spatial Diversity for Region-based Active Learningcited by 1
  26. 2025Decomposition Sampling for Efficient Region Annotations in Active Learningcited by 1
  27. 2021Embodied Learning for Lifelong Visual Perceptioncited by 0
  28. 2022Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learningcited by 0
  29. 2023Maturity-Aware Active Learning for Semantic Segmentation with Hierarchically-Adaptive Sample Assessmentcited by 0
  30. 2024Modular Deep Active Learning Framework for Image Annotation: A Technical Report for the Ophthalmo-AI Projectcited by 0
  31. 2026ActiveFreq: Integrating Active Learning and Frequency Domain Analysis for Interactive Segmentationcited by 0
  32. 2026RareSpot+: A Benchmark, Model, and Active Learning Framework for Small and Rare Wildlife in Aerial Imagerycited by 0
  33. 2026iSAGE: A Human-in-the-Loop Framework for Remote Sensing Semantic Segmentation via Sparse Point Supervisioncited by 0
  34. 2026Preserve the Hard, Regenerate the Rest: Uncertainty-Guided Synthetic Training Data Augmentation with Diffusion Modelscited by 0
  35. 2026Cost-efficient Active Learning for Referring Image Segmentation and Groundingcited by 0
  36. 2026SPARCL: Spatially Adaptive Regional Coverage for Active Learning in Chest X-ray Analysiscited by 0
  37. 2026From Human Corrections to Learned Segmentations: A Critical Mathematical Review of Information, Learning, and Human Effortcited by 0