mlsanityData to 14 Oct 2026

Fully Convolutional Multi-Class Multiple Instance Learning

arXiv 1412.7144 ↗First posted 22 Dec 2014cs.CV cs.LG cs.NEcited by 37

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Abstract (arXiv)

Multiple instance learning (MIL) can reduce the need for costly annotation in tasks such as semantic segmentation by weakening the required degree of supervision. We propose a novel MIL formulation of multi-class semantic segmentation learning by a fully convolutional network. In this setting, we seek to learn a semantic segmentation model from just weak image-level labels. The model is trained end-to-end to jointly optimize the representation while disambiguating the pixel-image label assignment. Fully convolutional training accepts inputs of any size, does not need object proposal pre-processing, and offers a pixelwise loss map for selecting latent instances. Our multi-class MIL loss exploits the further supervision given by images with multiple labels. We evaluate this approach through preliminary experiments on the PASCAL VOC segmentation challenge.

Key facts. Cited by 37 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Dec 2014.

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Citing papers per year: 2021: 8; 2022: 9; 2023: 11; 2024: 2; 2025: 4; 2026: 3.

37 papers.

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  1. 2022DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classificationcited by 256
  2. 2021Mixed supervision for surface-defect detection: from weakly to fully supervised learningcited by 101
  3. 2022L2G: A Simple Local-to-Global Knowledge Transfer Framework for Weakly Supervised Semantic Segmentationcited by 57
  4. 2022Unsupervised Semantic Segmentation with Self-supervised Object-centric Representationscited by 54
  5. 2022LESS: Label-Efficient Semantic Segmentation for LiDAR Point Cloudscited by 41
  6. 2021Every Annotation Counts: Multi-label Deep Supervision for Medical Image Segmentationcited by 37
  7. 2023Referring Image Segmentation Using Text Supervisioncited by 35
  8. 2021Fully Convolutional Networks for Panoptic Segmentation with Point-based Supervisioncited by 31
  9. 2023Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentationcited by 24
  10. 2021A Spacecraft Dataset for Detection, Segmentation and Parts Recognitioncited by 23
  11. 2023Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learningcited by 23
  12. 2023An Alternative to WSSS? An Empirical Study of the Segment Anything Model (SAM) on Weakly-Supervised Semantic Segmentation Problemscited by 19
25 more
  1. 2023Regression-based Deep-Learning predicts molecular biomarkers from pathology slidescited by 15
  2. 2023USAGE: A Unified Seed Area Generation Paradigm for Weakly Supervised Semantic Segmentationcited by 14
  3. 2021Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised Segmentationcited by 13
  4. 2023SOAR: Scene-debiasing Open-set Action Recognitioncited by 12
  5. 2021Latent Space Regularization for Unsupervised Domain Adaptation in Semantic Segmentationcited by 10
  6. 2023ZScribbleSeg: Zen and the Art of Scribble Supervised Medical Image Segmentationcited by 8
  7. 2022Mixed-UNet: Refined Class Activation Mapping for Weakly-Supervised Semantic Segmentation with Multi-scale Inferencecited by 7
  8. 2023Label-efficient Segmentation via Affinity Propagationcited by 7
  9. 2022Bag Graph: Multiple Instance Learning using Bayesian Graph Neural Networkscited by 6
  10. 2021Differentiable Projection for Constrained Deep Learningcited by 4
  11. 2023Open Vocabulary Semantic Scene Sketch Understandingcited by 4
  12. 2025Weakly-Supervised Image Forgery Localization via Vision-Language Collaborative Reasoning Frameworkcited by 4
  13. 2023Boundary-RL: Reinforcement Learning for Weakly-Supervised Prostate Segmentation in TRUS Imagescited by 3
  14. 2024Key Patches Are All You Need: A Multiple Instance Learning Framework For Robust Medical Diagnosiscited by 3
  15. 2024Partial Scene Text Retrievalcited by 3
  16. 2022ISIM: Iterative Self-Improved Model for Weakly Supervised Segmentationcited by 2
  17. 2022Weakly-supervised detection of AMD-related lesions in color fundus images using explainable deep learningcited by 2
  18. 2025Context-Aware Weakly Supervised Image Manipulation Localization with SAM Refinementcited by 2
  19. 2022Go Beyond Multiple Instance Neural Networks: Deep-learning Models based on Local Pattern Aggregationcited by 1
  20. 2025SketchYourSeg: Mask-Free Subjective Image Segmentation via Freehand Sketchescited by 1
  21. 2026CIEC: Coupling Implicit and Explicit Cues for Multimodal Weakly Supervised Manipulation Localizationcited by 1
  22. 2021Few-shot Visual Relationship Co-localizationcited by 0
  23. 2025Emerging Semantic Segmentation from Positive and Negative Coarse Label Learningcited by 0
  24. 2026SAPL: Semantic-Agnostic Prompt Learning in CLIP for Weakly Supervised Image Manipulation Localizationcited by 0
  25. 2026PLESS: Pseudo-Label Enhancement with Spreading Scribbles for Weakly Supervised Segmentationcited by 0