mlsanityData to 14 Oct 2026

Noisy Annotations in Semantic Segmentation

arXiv 2406.10891 ↗First posted 16 Jun 2024cs.CV cs.LGcited by 5

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Abstract (arXiv)

Obtaining accurate labels for instance segmentation is particularly challenging due to the complex nature of the task. Each image necessitates multiple annotations, encompassing not only the object class but also its precise spatial boundaries. These requirements elevate the likelihood of errors and inconsistencies in both manual and automated annotation processes. By simulating different noise conditions, we provide a realistic scenario for assessing the robustness and generalization capabilities of instance segmentation models in different segmentation tasks, introducing COCO-N and Cityscapes-N. We also propose a benchmark for weakly annotation noise, dubbed COCO-WAN, which utilizes foundation models and weak annotations to simulate semi-automated annotation tools and their noisy labels. This study sheds light on the quality of segmentation masks produced by various models and challenges the efficacy of popular methods designed to address learning with label noise.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 22 of its 37 references that are in the map. First posted on arXiv 16 Jun 2024.

Builds on

22 of 37 references.

References · 22

  1. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  2. 2023Segment Anythingcited by 8,234
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  10. 2020Learning from Noisy Labels with Deep Neural Networks: A Surveycited by 582
  11. 2019Symmetric Cross Entropy for Robust Learning with Noisy Labelscited by 500
  12. 2019Hybrid Task Cascade for Instance Segmentationcited by 481
10 more
  1. 2019Unsupervised Label Noise Modeling and Loss Correctioncited by 328
  2. 2019Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentationcited by 260
  3. 2019Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiographycited by 244
  4. 2021Boundary IoU: Improving Object-Centric Image Segmentation Evaluationcited by 188
  5. 2019Deep learning with noisy labels: exploring techniques and remedies in medical image analysiscited by 173
  6. 2017Playing for Benchmarkscited by 169
  7. 2021Robustness and Reliability When Training With Noisy Labelscited by 10
  8. 2023Semi-Supervised Semantic Segmentation via Marginal Contextual Informationcited by 8
  9. 2023How to Efficiently Annotate Images for Best-Performing Deep Learning Based Segmentation Models: An Empirical Study with Weak and Noisy Annotations and Segment Anything Modelcited by 7
  10. 2022On Image Segmentation With Noisy Labels: Characterization and Volume Properties of the Optimal Solutions to Accuracy and Dicecited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 1.

5 papers.

  1. 2024Hysteresis Activation Function for Efficient Inferencecited by 2
  2. 2025Benchmarking Adversarial Patch Selection and Locationcited by 1
  3. 2025Segment-to-Act: Label-Noise-Robust Action-Prompted Video Segmentation Towards Embodied Intelligencecited by 1
  4. 2025R3: Reconstruction, Raw, and Rain: Deraining Directly in the Bayer Domaincited by 0
  5. 2026A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA)cited by 0