mlsanityData to 7 Oct 2026

Active Label Correction for Semantic Segmentation with Foundation Models

arXiv 2403.10820 ↗First posted 16 Mar 2024cs.CVcited by 7

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Abstract (arXiv)

Training and validating models for semantic segmentation require datasets with pixel-wise annotations, which are notoriously labor-intensive. Although useful priors such as foundation models or crowdsourced datasets are available, they are error-prone. We hence propose an effective framework of active label correction (ALC) based on a design of correction query to rectify pseudo labels of pixels, which in turn is more annotator-friendly than the standard one inquiring to classify a pixel directly according to our theoretical analysis and user study. Specifically, leveraging foundation models providing useful zero-shot predictions on pseudo labels and superpixels, our method comprises two key techniques: (i) an annotator-friendly design of correction query with the pseudo labels, and (ii) an acquisition function looking ahead label expansions based on the superpixels. Experimental results on PASCAL, Cityscapes, and Kvasir-SEG datasets demonstrate the effectiveness of our ALC framework, outperforming prior methods for active semantic segmentation and label correction. Notably, utilizing our method, we obtained a revised dataset of PASCAL by rectifying errors in 2.6 million pixels in PASCAL dataset.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 34 of its 49 references that are in the map. First posted on arXiv 16 Mar 2024.

Builds on

34 of 49 references.

References · 34

  1. 2023Segment Anythingcited by 8,158
  2. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
  3. 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,274
  4. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,210
  5. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,320
  6. 2018Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labelscited by 1,053
  7. 2018Learning to Reweight Examples for Robust Deep Learningcited by 670
  8. 2016Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approachcited by 663
  9. 2019Kvasir-SEG: A Segmented Polyp Datasetcited by 588
  10. 2020Learning from Noisy Labels with Deep Neural Networks: A Surveycited by 580
  11. 2018Bag of Tricks for Image Classification with Convolutional Neural Networkscited by 430
  12. 2019Confident Learning: Estimating Uncertainty in Dataset Labelscited by 411
22 more
  1. 2021Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarkscited by 388
  2. 2019Variational Adversarial Active Learningcited by 284
  3. 2017Cost-Effective Active Learning for Deep Image Classificationcited by 165
  4. 2017Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentationcited by 145
  5. 2019ViewAL: Active Learning with Viewpoint Entropy for Semantic Segmentationcited by 95
  6. 2013SEEDS: Superpixels Extracted via Energy-Driven Samplingcited by 75
  7. 2023Token Contrast for Weakly-Supervised Semantic Segmentationcited by 64
  8. 2020Reinforced active learning for image segmentationcited by 56
  9. 2023AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weekscited by 52
  10. 2018CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentationcited by 47
  11. 2021Active label cleaning for improved dataset quality under resource constraintscited by 44
  12. 2023SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Modelcited by 38
  13. 2021Low Budget Active Learning via Wasserstein Distance: An Integer Programming Approachcited by 34
  14. 2021All you need are a few pixels: semantic segmentation with PixelPickcited by 30
  15. 2019Identifying Mislabeled Instances in Classification Datasetscited by 26
  16. 2022Automated Detection of Label Errors in Semantic Segmentation Datasets via Deep Learning and Uncertainty Quantificationcited by 20
  17. 2023Streaming Active Learning with Deep Neural Networkscited by 20
  18. 2018MedAL: Deep Active Learning Sampling Method for Medical Image Analysiscited by 19
  19. 2023Adaptive Superpixel for Active Learning in Semantic Segmentationcited by 14
  20. 2023Estimating label quality and errors in semantic segmentation data via any modelcited by 12
  21. 2020One-bit Supervision for Image Classificationcited by 10
  22. 2023Active Learning for Semantic Segmentation with Multi-class Label Querycited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 2.

7 papers.

  1. 2025Enhancing Cost Efficiency in Active Learning with Candidate Set Querycited by 3
  2. 2024Active Prompt Learning with Vision-Language Model Priorscited by 2
  3. 2025A2LC: Active and Automated Label Correction for Semantic Segmentationcited by 2
  4. 2025From Label Error Detection to Correction: A Modular Framework and Benchmark for Object Detection Datasetscited by 0
  5. 2025Learning to Detect Label Errors by Making Them: A Method for Segmentation and Object Detection Datasetscited by 0
  6. 2026iSAGE: A Human-in-the-Loop Framework for Remote Sensing Semantic Segmentation via Sparse Point Supervisioncited by 0
  7. 2026Cost-efficient Active Learning for Referring Image Segmentation and Groundingcited by 0