mlsanityData to 7 Oct 2026

Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation

arXiv 1502.02734 ↗First posted 9 Feb 2015cs.CVcited by 189

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Abstract (arXiv)

Deep convolutional neural networks (DCNNs) trained on a large number of images with strong pixel-level annotations have recently significantly pushed the state-of-art in semantic image segmentation. We study the more challenging problem of learning DCNNs for semantic image segmentation from either (1) weakly annotated training data such as bounding boxes or image-level labels or (2) a combination of few strongly labeled and many weakly labeled images, sourced from one or multiple datasets. We develop Expectation-Maximization (EM) methods for semantic image segmentation model training under these weakly supervised and semi-supervised settings. Extensive experimental evaluation shows that the proposed techniques can learn models delivering competitive results on the challenging PASCAL VOC 2012 image segmentation benchmark, while requiring significantly less annotation effort. We share source code implementing the proposed system at https://bitbucket.org/deeplab/deeplab-public.

Key facts. Cited by 189 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Feb 2015.

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Citing papers per year: 2021: 72; 2022: 37; 2023: 40; 2024: 20; 2025: 16; 2026: 4.

189 papers.

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  1. 2021A Survey on Deep Semi-supervised Learningcited by 250
  2. 2021Unsupervised Semantic Segmentation by Contrasting Object Mask Proposalscited by 190
  3. 2022Panoptic NeRF: 3D-to-2D Label Transfer for Panoptic Urban Scene Segmentationcited by 152
  4. 2021TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localizationcited by 122
  5. 2022Learning Affinity from Attention: End-to-End Weakly-Supervised Semantic Segmentation with Transformerscited by 118
  6. 2022CLIP is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic Segmentationcited by 118
  7. 2021Semi-supervised Semantic Segmentation with Directional Context-aware Consistencycited by 112
  8. 2021BBAM: Bounding Box Attribution Map for Weakly Supervised Semantic and Instance Segmentationcited by 104
  9. 2022Cross Language Image Matching for Weakly Supervised Semantic Segmentationcited by 72
  10. 2021DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervisioncited by 59
  11. 2022L2G: A Simple Local-to-Global Knowledge Transfer Framework for Weakly Supervised Semantic Segmentationcited by 57
  12. 2022Discovering Object Masks with Transformers for Unsupervised Semantic Segmentationcited by 54
38 more
  1. 2021Scalable Scene Flow from Point Clouds in the Real Worldcited by 49
  2. 2021Revisiting Contrastive Methods for Unsupervised Learning of Visual Representationscited by 48
  3. 2021Affinity Attention Graph Neural Network for Weakly Supervised Semantic Segmentationcited by 47
  4. 2021Unveiling the Potential of Structure Preserving for Weakly Supervised Object Localizationcited by 46
  5. 2021Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learningcited by 43
  6. 2021Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentationcited by 41
  7. 2021Incremental Learning in Semantic Segmentation from Image Labelscited by 39
  8. 2021Every Annotation Counts: Multi-label Deep Supervision for Medical Image Segmentationcited by 37
  9. 2021Pseudo-labeling for Scalable 3D Object Detectioncited by 32
  10. 2021Fully Convolutional Networks for Panoptic Segmentation with Point-based Supervisioncited by 31
  11. 2021All you need are a few pixels: semantic segmentation with PixelPickcited by 30
  12. 2023DaTaSeg: Taming a Universal Multi-Dataset Multi-Task Segmentation Modelcited by 30
  13. 2023Segment Anything is A Good Pseudo-label Generator for Weakly Supervised Semantic Segmentationcited by 27
  14. 2021Unsupervised Foreground Extraction via Deep Region Competitioncited by 26
  15. 2023Semantic Image Segmentation: Two Decades of Researchcited by 26
  16. 2021Beyond Semantic to Instance Segmentation: Weakly-Supervised Instance Segmentation via Semantic Knowledge Transfer and Self-Refinementcited by 25
  17. 2021An EM Framework for Online Incremental Learning of Semantic Segmentationcited by 24
  18. 2023LEOD: Label-Efficient Object Detection for Event Camerascited by 24
  19. 2021Adaptive Affinity Loss and Erroneous Pseudo-Label Refinement for Weakly Supervised Semantic Segmentationcited by 22
  20. 2022MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency Regularizationcited by 22
  21. 2022A Survey on Deep Learning for Skin Lesion Segmentationcited by 22
  22. 2022From colouring-in to pointillism: revisiting semantic segmentation supervisioncited by 22
  23. 2021Medical Image Segmentation with Limited Supervision: A Review of Deep Network Modelscited by 21
  24. 2021Efficient Algorithms for Learning from Coarse Labelscited by 21
  25. 2021Background-Click Supervision for Temporal Action Localizationcited by 21
  26. 2022Learning Self-Supervised Low-Rank Network for Single-Stage Weakly and Semi-Supervised Semantic Segmentationcited by 21
  27. 2022Weakly-supervised segmentation of referring expressionscited by 21
  28. 2021Weakly-supervised Instance Segmentation via Class-agnostic Learning with Salient Imagescited by 20
  29. 2023Mask-Free Video Instance Segmentationcited by 20
  30. 2021Weakly Supervised Instance Segmentation for Videos with Temporal Mask Consistencycited by 19
  31. 2022Weakly-Supervised Semantic Segmentation with Visual Words Learning and Hybrid Poolingcited by 19
  32. 2023Weakly-Supervised Semantic Segmentation with Image-Level Labels: from Traditional Models to Foundation Modelscited by 19
  33. 2023Weakly-Supervised Audio-Visual Segmentationcited by 19
  34. 2025SAMRefiner: Taming Segment Anything Model for Universal Mask Refinementcited by 19
  35. 2021Weakly supervised segmentation with cross-modality equivariant constraintscited by 18
  36. 2021Weakly Supervised Semantic Segmentation via Alternative Self-Dual Teachingcited by 18
  37. 2023Shepherding Slots to Objects: Towards Stable and Robust Object-Centric Learningcited by 17
  38. 2021Weakly-Supervised Hierarchical Models for Predicting Persuasive Strategies in Good-faith Textual Requestscited by 16

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