mlsanityData to 14 Oct 2026

Segment Anything is A Good Pseudo-label Generator for Weakly Supervised Semantic Segmentation

arXiv 2305.01275 ↗First posted 2 May 2023cs.CVcited by 27

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Abstract (arXiv)

Weakly supervised semantic segmentation with weak labels is a long-lived ill-posed problem. Mainstream methods mainly focus on improving the quality of pseudo labels. In this report, we attempt to explore the potential of 'prompt to masks' from the powerful class-agnostic large segmentation model, segment-anything. Specifically, different weak labels are used as prompts to the segment-anything model, generating precise class masks. The class masks are utilized to generate pseudo labels to train the segmentation networks. We have conducted extensive experiments on PASCAL VOC 2012 dataset. Experiments demonstrate that segment-anything can serve as a good pseudo-label generator. The code will be made publicly available.

Key facts. Cited by 27 papers in the map (data through 14 Oct 2026). Builds on 37 of its 46 references that are in the map. First posted on arXiv 2 May 2023.

Builds on

37 of 46 references.

References · 37

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  4. 2023Segment Anythingcited by 8,234
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25 more
  1. 2022EVA: Exploring the Limits of Masked Visual Representation Learning at Scalecited by 691
  2. 2019Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networkscited by 582
  3. 2016ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentationcited by 331
  4. 2018Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentationcited by 321
  5. 2020Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentationcited by 300
  6. 2015What's the Point: Semantic Segmentation with Point Supervisioncited by 298
  7. 2019Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relationscited by 247
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  12. 2015Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentationcited by 189
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  14. 2018Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentationcited by 145
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  16. 2021Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentationcited by 134
  17. 2021Railroad is not a Train: Saliency as Pseudo-pixel Supervision for Weakly Supervised Semantic Segmentationcited by 127
  18. 2022CLIP is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic Segmentationcited by 118
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 10; 2024: 12; 2025: 2; 2026: 3.

27 papers.

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  1. 2023A Comprehensive Survey on Segment Anything Model for Vision and Beyondcited by 64
  2. 2023Annotation-free Audio-Visual Segmentationcited by 46
  3. 2023A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineeringcited by 45
  4. 2024MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentationcited by 39
  5. 2023SAM-Assisted Remote Sensing Imagery Semantic Segmentation with Object and Boundary Constraintscited by 35
  6. 2023Foundation Model Assisted Weakly Supervised Semantic Segmentationcited by 33
  7. 2023Semantic-aware SAM for Point-Prompted Instance Segmentationcited by 16
  8. 2023Towards Training-free Open-world Segmentation via Image Prompt Foundation Modelscited by 15
  9. 2024WeakSAM: Segment Anything Meets Weakly-supervised Instance-level Recognitioncited by 10
  10. 2024iSeg: An Iterative Refinement-based Framework for Training-free Segmentationcited by 10
  11. 2025Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priorscited by 9
  12. 2024There is no SAMantics! Exploring SAM as a Backbone for Visual Understanding Taskscited by 7
15 more
  1. 2024Tuning-free Universally-Supervised Semantic Segmentationcited by 6
  2. 2023Industrial Segment Anything -- a Case Study in Aircraft Manufacturing, Intralogistics, Maintenance, Repair, and Overhaulcited by 4
  3. 2024Semantic Prompt Learning for Weakly-Supervised Semantic Segmentationcited by 3
  4. 2024DHR: Dual Features-Driven Hierarchical Rebalancing in Inter- and Intra-Class Regions for Weakly-Supervised Semantic Segmentationcited by 3
  5. 2024Segment Anything Model for automated image data annotation: empirical studies using text prompts from Grounding DINOcited by 3
  6. 2023Boosting Weakly-Supervised Image Segmentation via Representation, Transform, and Compensatorcited by 2
  7. 2023Segment Anything Model-guided Collaborative Learning Network for Scribble-supervised Polyp Segmentationcited by 2
  8. 2024Mixed-Query Transformer: A Unified Image Segmentation Architecturecited by 2
  9. 2024FusionVision: A comprehensive approach of 3D object reconstruction and segmentation from RGB-D cameras using YOLO and fast segment anythingcited by 1
  10. 2026Weakly Supervised Segmentation as Semantic-Based Regularizationcited by 1
  11. 2024Enhancing Weakly Supervised Semantic Segmentation with Multi-modal Foundation Models: An End-to-End Approachcited by 0
  12. 2024SinkSAM-Net: Knowledge-Driven Self-Supervised Sinkhole Segmentation Using Topographic Priors and Segment Anything Modelcited by 0
  13. 2025Depth Edge Alignment Loss: DEALing with Depth in Weakly Supervised Semantic Segmentationcited by 0
  14. 2026SAPNet++: Evolving Point-Prompted Instance Segmentation with Semantic and Spatial Awarenesscited by 0
  15. 2026Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcitycited by 0