Weakly Supervised Learning

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Overview

Weakly supervised learning studies how to train useful models when labels are missing, noisy, ambiguous, or available only for groups of examples rather than individual instances. Common approaches include multiple-instance learning, positive-unlabeled and partial-label learning, learning from label proportions, and pseudo-labeling or combining heuristic labeling functions. These methods support tasks such as finding objects in images with only image-level labels, locating events in videos with sparse timestamps, and classifying medical scans from patient-level diagnoses. A central challenge is that weak labels can encode bias or shift across settings, so reliable evaluation and deciding when a label or localized prediction can be trusted remain open concerns.

Latest papers

CardsList

156 papers20 papers in the last four weeks, level with the four weeks before. 0.1% of all new papers.

  1. Sounding Surgical Instrument Localisation through Audio-Visual Correspondence

    Oct 23, 2026Dominic Behling, Jonas Hein, Matthias SeiboldSurgical Instrument TrackingAudio-Visual Understanding

    Research in Orthopedic Computer Science (ROCS), Balgrist University Hospital, University of Zurich, Zurich, Switzerland. · Eberhard Karls Universität Tübingen, Germany.

  2. STEP: Surgical Point Tracking from Sparse Endpoints and Pseudo-trajectories

    Oct 22, 2026Bing Fan, Anwesa Choudhuri, Zhongpai Gao +5Point TrackingWeakly Supervised Learning

    United Imaging Intelligence, Boston, MA, USA · University of North Texas, Denton, TX, USA

  3. Soft Ontology Priors over Latent Concept Posteriors for Weakly Supervised Neuro-Symbolic Learning

    Oct 21, 2026Tengwei Song, Robert HoehndorfNeurosymbolic LearningWeakly Supervised Learning

    CEMSE, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

  4. IECG: Weakly-Supervised Interpretability-Enhancing Foundation Model for Electrocardiogram

    Oct 20, 2026Huynh Dang Nguyen, Trong-Thang Pham, Anh-Thuan Do +4Electrocardiogram Foundation ModelsElectrocardiogram Interpretation

    Quantum AI & Cyber Security Institute (QACI) - FPT Corporation, Vietnam · University of Arkansas, USA · Hospital Haiphong Medical University, Vietnam +2

  5. Beyond the Dominant Label: Recovering Coexisting Lesion Evidence with Multiple Instance Learning

    Oct 15, 2026Lubin Gan, Jing Zhang, Xinming Zhang +3Multiple Instance LearningWhole-Slide Image Analysis

    USTC, Anhui, China · Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing, Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Anhui, China

  6. Rethinking Learning from Label Proportions via Moment Matching

    Oct 15, 2026Tianhao Ma, Wei Wang, Yivan Zhang +4Weakly Supervised Learning

    The University of Tokyo · RIKEN AIP

  7. LLM-Based Weak Annotation for Cybersecurity Tweet Classification: A Teacher-Student Framework

    Oct 15, 2026Ferry Afit Kurniawan, Kazushi Okamoto, Koki Karube +2LLM-Assisted AnnotationText Classification

    Department of Informatics, Graduate School of Informatics and Engineering, The University of Electro-Communications 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan

  8. RespFocus: Learning Temporal Evidence for Weakly Supervised Respiratory Sound Classification

    Oct 11, 2026Yi Tian, Xiaohuan Chen, Yufei tang +5Respiratory Sound ClassificationWeakly Supervised Learning

    Fuyao University of Science and Technology (fyust.edu.cn) · Xiamen University (xmu.edu.cn)

  9. ProtoPur: Prototype-Purity Pseudo-Labeling for Weakly Supervised Instance Learning in Pathology

    Oct 11, 2026Chen Shu, Chengyang Zhang, Boyu Fu +5Weakly Supervised LearningComputational Pathology

    College of Computer Science, Sichuan University, Chengdu, China · Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, China · Pittsburgh Institute, Sichuan University, Chengdu, China +1

  10. Learning Proposition-Level Links from Sentence-Level Supervision for Neuro-Symbolic NLI

    Oct 10, 2026Xuyao Feng, Antonis BikakisNatural Language InferenceNeuro-Symbolic Reasoning

    University College London, United Kingdom

  11. Model-agnostic Knowledge Augmented Machine Learning for Weakly Annotated Bioimaging Data

    Oct 9, 2026Jayanie Bogahawatte, Sachith Seneviratne, Simon Maksour +6Weakly Supervised Learning

    AI, Optimization and Pattern Recognition Research Group, Faculty of Engineering and Information Technology, The University of Melbourne, Melbourne, Victoria, Australia · School of Science and Molecular Horizons, University of Wollongong, Northfields Avenue, New South Wales, Australia · Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia +3

  12. A Proof-of-Concept Study of Weakly Supervised Labeling of Fine-Grained EEG Components for Artifact Attenuation

    Oct 7, 2026Lu Wang-Nöth, Hai Huang, Philipp Heiler +3EEG Artifact RemovalElectroencephalography

    Institute for Applied Computer Science, University of the Bundeswehr Munich, Munich, Germany · brainboost GmbH, Munich, Germany · Graduate School of Engineering Science, The University of Osaka, Osaka, Japan +1

  13. STORK: Spatio-Temporal Observation of uterine contRactions via neural networKs

    Oct 7, 2026Melissa Schween, Tristan Gottwald, Jordina Aviles Verdera +3Fetal MRIMagnetic Resonance Imaging

    CAIMED, Leibniz University Hannover, Hannover, Germany · L3S, Leibniz University Hannover, Hannover, Germany · Institute of Information Processing, Leibniz University Hannover, Germany +1

  14. What Frame-Level Labels Can and Cannot Do for Small-UAV Point Detection in Thermal Video

    Oct 6, 2026Wonbin Son, Gyumum Choi, Junil Seo +1Infrared Small Target DetectionUAV Detection

  15. WildMatch: Weakly Supervised Image Matcher Adaptation for Wildlife Re-Identification

    Oct 5, 2026Turhan Can Kargin, Piotr Kubaty, Ekaterina Rostovskaya +3Animal Re-IdentificationImage Matching

    Faculty of Mathematics and Computer Science, Jagiellonian University, Poland · Doctoral School of Exact and Natural Sciences, Jagiellonian University, Poland · Institute of Environmental Sciences, Faculty of Biology, Jagiellonian University, Kraków, Poland +2

  16. Joint Class-Time Learning for Video Classification with Multi-Instance Partial-Label Learning

    Oct 5, 2026Lingyu Shen, Wei Tang, Fakhri Karray +1Weakly Supervised LearningMultiple Instance Learning

    School of Computer Science and Engineering, Southeast University, Nanjing 210096, China · Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab Emirates

  17. Spatial Supervision Without Attribution Optimization: Improving Post-Hoc Class Activation Maps via Box-Guided Evidence Routing

    Oct 5, 2026Wenhao Liang, Liangwei Nathan Zheng, Lin Yue +3Gradient-Based AttributionFeature Attribution

    Adelaide University, Adelaide, Australia

  18. Calibrated Weak Supervision for Post-Harvest Burned-Cropland Mapping Under Label Scarcity

    Oct 4, 2026Raunak Bhagate, Maitri Polisetty, R I MinuPost-Hoc CalibrationAgricultural Remote Sensing

    SRM Institute of Science & Technology

  19. EviDent-CBCT: Evidence-Bottlenecked Report Generation from Dental CBCT under Non-Exhaustive Report Supervision

    Oct 1, 2026Ruiyang Hao, Zhi Qin Tan, Yulan He +2Radiology Report GenerationMedical Report Generation

    King’s College London

  20. Less Supervision, Better Generalization: Weakly Supervised Fake Region Localization in Diffusion-Edited Images

    Sep 29, 2026Junhee Lee, Donghyeon Jeon, Taeoh Kim +2Image Forgery LocalizationWeakly Supervised Learning

    Kyung Hee University · NAVER Cloud

  21. Beyond Keywords: Leveraging Generative LLMs and Label Aggregation to Classify Economic Policy Uncertainty in News Articles

    Sep 25, 2026Paul TrustHierarchical ClassificationText Classification

  22. From Change Captions to Change Detection: Semantic-Appearance Agreement Framework for Remote Sensing Change Detection

    Sep 23, 2026Yuan Qian, Jie MaRemote Sensing Change DetectionRemote Sensing

    School of Information Science and Technology, Beijing Foreign Studies University, Beijing 100089, China

  23. Weakly Supervised Quantum Error Mitigation

    Sep 22, 2026Seyed Mohamad Ali Tousi, G. N. DeSouzaWeakly Supervised LearningQuantum Error Mitigation

    Vision-Guided and Intelligent Robotics Lab (ViGIR) University of Missouri Columbia, US

  24. SomaNet: Weakly Supervised Learning for Instance Soma Segmentation in 3D Electron Microscopy with Partial Annotations

    Sep 19, 2026Mohammad Khateri, Morteza Ghahremani, Jussi Tohka +1Electron MicroscopyInstance Segmentation

    A. I. Virtanen Institute for Molecular Sciences, Faculty of Health Sciences, University of Eastern Finland, Finland

  25. Building Trustworthy Mental Health Benchmarks on Bluesky: A Validation-Aware Weak-Supervision Framework

    Sep 19, 2026Gaurab Chhetri, Anandi Dutta, Subasish DasMental HealthSocial Media Analysis

    Department of Computer Science Texas State University San Marcos, Texas, USA · Ingram School of Engineering Texas State University San Marcos, Texas, USA

  26. AgroBench: A Reproducible Multimodal Benchmark for Weakly Supervised Crop Yield Learning from County Statistics and Pixel Observations

    Sep 16, 2026Udaiveer Singh, Rajiv Ranjan, Shashank Tamaskar +1Multi-Angular Remote SensingRemote Sensing Benchmarks

    Plaksha University, Mohali, Punjab, India · Purdue University, West Lafayette, Indiana, USA

  27. Multi-Label Proportion Learning for Sea-Ice Type Prediction

    Sep 14, 2026Samira Alkaee Taleghan, Younghyun Koo, Andrew P. Barrett +1Label Distribution LearningRemote Sensing

    University of Colorado Denver Denver, Colorado, USA · National Snow and Ice Data Center (NSIDC), CIRES, University of Colorado Boulder Boulder, Colorado, USA

  28. PU classification under Non-SCAR: clustering-assisted logistic model with oversampling enhancement

    Sep 13, 2026Konrad Furmańczyk, Kacper PaczutkowskiPositive-Unlabeled LearningWeakly Supervised Learning

    Institute of Information Technology, Warsaw University of Life Sciences, Warsaw, Poland

  29. Machine Learning under Imperfect Data: Challenges and Methods

    Sep 12, 2026Masoumeh ZareapoorLearning with Missing DataOOD Generalization

    Shanghai Jiao Tong University, China

  30. Soft Symbol Grounding for Prototypical Concepts

    Sep 10, 2026Marcos Galván-López, Nijesh Upreti, Hiram Calvo +2Prototype-Based ClassificationWeakly Supervised Learning

    Centro de Investigación en Computación, Instituto Politécnico Nacional. · School of Informatics, University of Edinburgh.