mlsanityData to 7 Oct 2026

ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation

arXiv 1604.05144 ↗First posted 18 Apr 2016cs.CVcited by 330

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Abstract (arXiv)

Large-scale data is of crucial importance for learning semantic segmentation models, but annotating per-pixel masks is a tedious and inefficient procedure. We note that for the topic of interactive image segmentation, scribbles are very widely used in academic research and commercial software, and are recognized as one of the most user-friendly ways of interacting. In this paper, we propose to use scribbles to annotate images, and develop an algorithm to train convolutional networks for semantic segmentation supervised by scribbles. Our algorithm is based on a graphical model that jointly propagates information from scribbles to unmarked pixels and learns network parameters. We present competitive object semantic segmentation results on the PASCAL VOC dataset by using scribbles as annotations. Scribbles are also favored for annotating stuff (e.g., water, sky, grass) that has no well-defined shape, and our method shows excellent results on the PASCAL-CONTEXT dataset thanks to extra inexpensive scribble annotations. Our scribble annotations on PASCAL VOC are available at http://research.microsoft.com/en-us/um/people/jifdai/downloads/scribble_sup

Key facts. Cited by 330 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Apr 2016.

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Citing papers per year: 2021: 78; 2022: 74; 2023: 82; 2024: 58; 2025: 21; 2026: 17.

330 papers.

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  1. 2021Unsupervised Semantic Segmentation by Contrasting Object Mask Proposalscited by 190
  2. 2024InstanceDiffusion: Instance-level Control for Image Generationcited by 175
  3. 2021Reviving Iterative Training with Mask Guidance for Interactive Segmentationcited by 162
  4. 2023DiffuMask: Synthesizing Images with Pixel-level Annotations for Semantic Segmentation Using Diffusion Modelscited by 162
  5. 2021Railroad is not a Train: Saliency as Pseudo-pixel Supervision for Weakly Supervised Semantic Segmentationcited by 127
  6. 2023DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Modelscited by 122
  7. 2022Learning What Not to Segment: A New Perspective on Few-Shot Segmentationcited by 120
  8. 2022Learning Affinity from Attention: End-to-End Weakly-Supervised Semantic Segmentation with Transformerscited by 118
  9. 2022CLIP is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic Segmentationcited by 118
  10. 2022ReCo: Retrieve and Co-segment for Zero-shot Transfercited by 115
  11. 2021Semi-supervised Semantic Segmentation with Directional Context-aware Consistencycited by 112
  12. 2021Pixel Contrastive-Consistent Semi-Supervised Semantic Segmentationcited by 101
38 more
  1. 2022BigDatasetGAN: Synthesizing ImageNet with Pixel-wise Annotationscited by 99
  2. 2021Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentationcited by 95
  3. 2021One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentationcited by 89
  4. 2022Self-supervised Image-specific Prototype Exploration for Weakly Supervised Semantic Segmentationcited by 87
  5. 2021Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrastcited by 85
  6. 2021Pointly-Supervised Instance Segmentationcited by 83
  7. 2021Leveraging Auxiliary Tasks with Affinity Learning for Weakly Supervised Semantic Segmentationcited by 77
  8. 2023Review of Large Vision Models and Visual Prompt Engineeringcited by 77
  9. 2021Complementary Patch for Weakly Supervised Semantic Segmentationcited by 75
  10. 2022Class Re-Activation Maps for Weakly-Supervised Semantic Segmentationcited by 75
  11. 2022Cross Language Image Matching for Weakly Supervised Semantic Segmentationcited by 72
  12. 2021Context Decoupling Augmentation for Weakly Supervised Semantic Segmentationcited by 69
  13. 2022Scribble-Supervised LiDAR Semantic Segmentationcited by 69
  14. 2022CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervisioncited by 64
  15. 2022Scribble-Supervised Medical Image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervisioncited by 64
  16. 2022Regional Semantic Contrast and Aggregation for Weakly Supervised Semantic Segmentationcited by 64
  17. 2021Adaptive Early-Learning Correction for Segmentation from Noisy Annotationscited by 61
  18. 2021DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervisioncited by 59
  19. 2021A Closer Look at Self-training for Zero-Label Semantic Segmentationcited by 57
  20. 2022L2G: A Simple Local-to-Global Knowledge Transfer Framework for Weakly Supervised Semantic Segmentationcited by 57
  21. 2022Discovering Object Masks with Transformers for Unsupervised Semantic Segmentationcited by 54
  22. 2022Unsupervised Semantic Segmentation with Self-supervised Object-centric Representationscited by 54
  23. 2021Exploiting a Joint Embedding Space for Generalized Zero-Shot Semantic Segmentationcited by 53
  24. 2023Segment Anything Model (SAM) Enhanced Pseudo Labels for Weakly Supervised Semantic Segmentationcited by 53
  25. 2024Diffusion Models and Representation Learning: A Surveycited by 53
  26. 2021Pseudo-mask Matters in Weakly-supervised Semantic Segmentationcited by 48
  27. 2021Affinity Attention Graph Neural Network for Weakly Supervised Semantic Segmentationcited by 47
  28. 2022Tree Energy Loss: Towards Sparsely Annotated Semantic Segmentationcited by 47
  29. 2022A Survey on Label-efficient Deep Image Segmentation: Bridging the Gap between Weak Supervision and Dense Predictioncited by 46
  30. 2021Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learningcited by 43
  31. 2021Multi-Layer Pseudo-Supervision for Histopathology Tissue Semantic Segmentation using Patch-level Classification Labelscited by 43
  32. 2022ACSeg: Adaptive Conceptualization for Unsupervised Semantic Segmentationcited by 42
  33. 2021Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentationcited by 41
  34. 2023CLIP in Medical Imaging: A Surveycited by 40
  35. 2021Incremental Learning in Semantic Segmentation from Image Labelscited by 39
  36. 2021Puzzle-CAM: Improved localization via matching partial and full featurescited by 37
  37. 2022Threshold Matters in WSSS: Manipulating the Activation for the Robust and Accurate Segmentation Model Against Thresholdscited by 37
  38. 2022Expansion and Shrinkage of Localization for Weakly-Supervised Semantic Segmentationcited by 37

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