Data to 7 Oct 2026

Simple Does It: Weakly Supervised Instance and Semantic Segmentation

arXiv 1603.07485 ↗First posted 24 Mar 2016cs.CVcited by 226

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Abstract (arXiv)

Semantic labelling and instance segmentation are two tasks that require particularly costly annotations. Starting from weak supervision in the form of bounding box detection annotations, we propose a new approach that does not require modification of the segmentation training procedure. We show that when carefully designing the input labels from given bounding boxes, even a single round of training is enough to improve over previously reported weakly supervised results. Overall, our weak supervision approach reaches ~95% of the quality of the fully supervised model, both for semantic labelling and instance segmentation.

Key facts. Cited by 226 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Mar 2016.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 67; 2022: 66; 2023: 41; 2024: 32; 2025: 15; 2026: 5.

226 papers.

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2021Unsupervised Semantic Segmentation by Contrasting Object Mask Proposalscited by 190
2022Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localizationcited by 150
2021Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentationcited by 134
2021Railroad is not a Train: Saliency as Pseudo-pixel Supervision for Weakly Supervised Semantic Segmentationcited by 127
2022ReCo: Retrieve and Co-segment for Zero-shot Transfercited by 115
2021BBAM: Bounding Box Attribution Map for Weakly Supervised Semantic and Instance Segmentationcited by 104
2022FreeSOLO: Learning to Segment Objects without Annotationscited by 102
2021Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentationcited by 95
2021Weakly Supervised Object Localization and Detection: A Surveycited by 84
2021Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labelingcited by 84
2021Pointly-Supervised Instance Segmentationcited by 83
2021Complementary Patch for Weakly Supervised Semantic Segmentationcited by 75

38 more

2021Context Decoupling Augmentation for Weakly Supervised Semantic Segmentationcited by 69
2022CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervisioncited by 64
2022Regional Semantic Contrast and Aggregation for Weakly Supervised Semantic Segmentationcited by 64
2021DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervisioncited by 59
2022Weakly Supervised Semantic Segmentation using Out-of-Distribution Datacited by 58
2022Discovering Object Masks with Transformers for Unsupervised Semantic Segmentationcited by 54
2022Unsupervised Semantic Segmentation with Self-supervised Object-centric Representationscited by 54
2023Segment Anything Model (SAM) Enhanced Pseudo Labels for Weakly Supervised Semantic Segmentationcited by 53
2021Pseudo-mask Matters in Weakly-supervised Semantic Segmentationcited by 48
2021Affinity Attention Graph Neural Network for Weakly Supervised Semantic Segmentationcited by 47
2022A Survey on Label-efficient Deep Image Segmentation: Bridging the Gap between Weak Supervision and Dense Predictioncited by 46
2021Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learningcited by 43
2021Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentationcited by 41
2022Box-supervised Instance Segmentation with Level Set Evolutioncited by 41
2023CLIP in Medical Imaging: A Surveycited by 40
2021Unsupervised Discovery of the Long-Tail in Instance Segmentation Using Hierarchical Self-Supervisioncited by 39
2021Incremental Learning in Semantic Segmentation from Image Labelscited by 39
2021Puzzle-CAM: Improved localization via matching partial and full featurescited by 37
2021Every Annotation Counts: Multi-label Deep Supervision for Medical Image Segmentationcited by 37
2022Threshold Matters in WSSS: Manipulating the Activation for the Robust and Accurate Segmentation Model Against Thresholdscited by 37
2022H2RBox: Horizontal Box Annotation is All You Need for Oriented Object Detectioncited by 36
2023SkyEye: Self-Supervised Bird's-Eye-View Semantic Mapping Using Monocular Frontal View Imagescited by 35
2023Conflict-Based Cross-View Consistency for Semi-Supervised Semantic Segmentationcited by 35
2021NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentationcited by 33
2021Fully Convolutional Networks for Panoptic Segmentation with Point-based Supervisioncited by 31
2022BoxTeacher: Exploring High-Quality Pseudo Labels for Weakly Supervised Instance Segmentationcited by 31
2021All you need are a few pixels: semantic segmentation with PixelPickcited by 30
2023DaTaSeg: Taming a Universal Multi-Dataset Multi-Task Segmentation Modelcited by 30
2021Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentationcited by 28
2023H2RBox-v2: Incorporating Symmetry for Boosting Horizontal Box Supervised Oriented Object Detectioncited by 28
2023Segment Anything is A Good Pseudo-label Generator for Weakly Supervised Semantic Segmentationcited by 27
2021Unsupervised Foreground Extraction via Deep Region Competitioncited by 26
2022TransCAM: Transformer Attention-based CAM Refinement for Weakly Supervised Semantic Segmentationcited by 26
2022ShapePU: A New PU Learning Framework Regularized by Global Consistency for Scribble Supervised Cardiac Segmentationcited by 26
2022Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentationcited by 26
2023Semantic Image Segmentation: Two Decades of Researchcited by 26
2021Beyond Semantic to Instance Segmentation: Weakly-Supervised Instance Segmentation via Semantic Knowledge Transfer and Self-Refinementcited by 25
2023Shatter and Gather: Learning Referring Image Segmentation with Text Supervisioncited by 25

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