Self-Supervised Visual Representation Learning

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Overview

The field studies how visual models can learn useful features without relying on large collections of human-labeled images or videos, enabling transfer to tasks such as classification, segmentation, recognition, and robotic control. Common approaches include contrastive learning across augmented views, masked autoencoders that reconstruct hidden content, self-distillation, and joint-embedding predictive architectures that predict latent features; video and multi-view methods also use temporal and geometric consistency. These methods are evaluated on benchmarks such as ImageNet and COCO, as well as video, 3D, and specialized medical or remote-sensing tasks, with large pretrained encoders often adapted using lightweight probes or fine-tuning. A central tension is how to learn features that are both broadly transferable and sensitive to spatial detail, motion, geometry, or domain-specific cues, while avoiding shortcuts and representation collapse.

Latest papers

CardsList

237 papers37 papers in the last four weeks, up 48% on the four weeks before. 0.2% of all new papers.

  1. OceanMAE: A Foundation Model for Ocean Remote Sensing

    Apr 9, 2026Viola-Joanna Stamer, Panagiotis Agrafiotis, Behnood Rasti +1Remote SensingSelf-Supervised Visual Representation Learning

    Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin, Germany and with the Berlin Institute for the Foundations of Learning and Data (BIFOLD), 10623 Berlin, Germany.

  2. A Mixture of Experts Foundation Model for Scanning Electron Microscopy Image Analysis

    Apr 7, 2026Sk Miraj Ahmed, Yuewei Lin, Chuntian Cao +9Microscopy Image AnalysisElectron Microscopy

    Computing and Data Sciences, Brookhaven National Laboratory, Upton, 11973, New York, USA. · Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, 11973, New York, USA. · Department of Material Sciences and Engineering, The University of Texas at Dallas, Richardson, 75080, Texas, USA.

  3. Group-DINOmics: Incorporating People Dynamics into DINO for Self-supervised Group Activity Feature Learning

    Apr 6, 2026Ryuki Tezuka, Chihiro Nakatani, Norimichi UkitaRepresentation LearningHuman Activity Recognition

    Toyota Technological Institute

  4. CraterBench-R: Instance-Level Crater Retrieval for Planetary Scale

    Apr 6, 2026Jichao Fang, Lei Zhang, Michael Phillips +1Image RetrievalImage Matching

    Northern Illinois University · University of Arizona

  5. Learning Robust Visual Features in Computed Tomography Enables Efficient Transfer Learning for Clinical Tasks

    Apr 5, 2026Rubén Moreno-Aguado, Alba Magallón, Victor Moreno +2Medical Imaging Foundation ModelsMedical Image Representation Learning

    Bioengineering Department and Imperial-X, Imperial College London, London, UK · Department of Computer Science, University of Manchester, Manchester, UK · Oncology Data Analytics Program, Catalan Institute of Oncology, Barcelona, Spain +6

  6. Learning ECG Image Representations via Dual Physiological-Aware Alignments

    Apr 2, 2026Hung Manh Pham, Jialu Tang, Aaqib Saeed +3Electrocardiogram Representation LearningMedical Image Representation Learning

    University of Cambridge · Singapore Management University · Eindhoven University of Technology, Netherlands

  7. Suppressing Non-Semantic Noise in Masked Image Modeling Representations

    Mar 31, 2026Martine Hjelkrem-Tan, Marius Aasan, Rwiddhi Chakraborty +3Self-Supervised Visual Representation LearningMasked Image Modeling

    University of Oslo · UiT The Arctic University of Norway

  8. STRADAViT: Self-Supervised Domain Adaptation of Vision Transformer Backbones for Radio Astronomy

    Mar 31, 2026Andrea DeMarco, Ian Fenech Conti, Hayley Camilleri +2Radio AstronomyDomain-Adaptive Pretraining

    Institute of Space Sciences and Astronomy, University of Malta, Msida, MSD2080, Malta · School of Physics and Astronomy, Royal Observatory, University of Edinburgh, Edinburgh, United Kingdom · Istituto Nazionale di Astrofisica (INAF), Osservatorio Astrofisico di Catania, Catania, Italy

  9. To View Transform or Not to View Transform: NeRF-based Pre-training Perspective

    Mar 30, 2026Hyeonjun Jeong, Juyeb Shin, Dongsuk Kum3D ReconstructionAutonomous Driving Perception

    KAIST, Daejeon, Korea

  10. BINO: Encoder Centric Self Supervised Stereo With Native Pair Input

    Mar 29, 2026Haokun ZhouVisual Representation LearningStereo Matching

    Imperial College London

  11. TrackMAE: Video Representation Learning via Track Mask and Predict

    Mar 28, 2026Renaud Vandeghen, Fida Mohammad Thoker, Marc Van Droogenbroeck +1Video Representation LearningSelf-Supervised Visual Representation Learning

    University of Li`ege · KAUST

  12. From Static to Dynamic: Exploring Self-supervised Image-to-Video Representation Transfer Learning

    Mar 27, 2026Yang Liu, Qianqian Xu, Peisong Wen +3Video Representation LearningTransfer Learning

    School of Computer Science and Technology, University of Chinese Academy of Sciences · State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences · Beijing Academy of Artificial Intelligence +3

  13. Finding Distributed Object-Centric Properties in Self-Supervised Transformers

    Mar 27, 2026Samyak Rawlekar, Amitabh Swain, Yujun Cai +3Unsupervised Visual Concept DiscoveryVision Transformer

    University of Illinois Urbana-Champaign · University of Queensland · UC Merced

  14. LEMON: a foundation model for nuclear morphology in Computational Pathology

    Mar 26, 2026Loïc Chadoutaud, Alice Blondel, Hana Feki +3Computational PathologyPathology Foundation Models

    Institut Curie, Paris, France · Mines Paris PSL, Centre for Computational Biology · INSERM U1331, Paris, France +6

  15. Gaussian Joint Embeddings For Self-Supervised Representation Learning

    Mar 26, 2026Yongchao HuangRepresentation LearningMultimodal Embedding

  16. CanViT: Toward Active-Vision Foundation Models

    Mar 23, 2026Yohaï-Eliel Berreby, Sabrina Du, Audrey Durand +1Active VisionActive Perception

    McGill University · Mila - Quebec AI Institute · Université Laval

  17. SpecTM: Spectral Targeted Masking for Trustworthy Foundation Models

    Mar 23, 2026Syed Usama Imtiaz, Mitra Nasr Azadani, Nasrin AlamdariMasked Image ModelingGeospatial Foundation Models

    Department of Civil and Environmental Engineering Florida State University, Tallahassee, FL, USA

  18. Towards Interpretable Foundation Models for Retinal Fundus Images

    Mar 19, 2026Samuel Ofosu Mensah, Camila Roa, Kerol Djoumessi +1Fundus ImagingMedical Imaging Foundation Models

    Hertie Institute for AI in Brain Health, University of Tübingen, Germany · Tübingen AI Center, University of Tübingen, Germany

  19. R2-Dreamer: Redundancy-Reduced World Models without Decoders or Augmentation

    Mar 18, 2026Naoki Morihira, Amal Nahar, Kartik Bharadwaj +3Model-Based RLWorld Model Learning

    Honda R&D Co., Ltd. · The University of Tokyo · RIKEN AIP

  20. SpikeCLR: Contrastive Self-Supervised Learning for Few-Shot Event-Based Vision using Spiking Neural Networks

    Mar 17, 2026Maxime Vaillant, Axel Carlier, Lai Xing Ng +2Self-Supervised Visual Representation LearningEvent-Based Vision

    CNRS, IPAL IRL 2955, Singapore · Université de Toulouse, IRIT, France · Institute for Infocomm Research, A*STAR, Singapore +2

  21. Self-Distillation of Hidden Layers for Self-Supervised Representation Learning

    Mar 16, 2026Scott C. Lowe, Anthony Fuller, Sageev Oore +2Self-Supervised Visual Representation LearningPredictive Representation Learning

    Vector Institute · Carleton University · Dalhousie University +2

  22. V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning

    Mar 15, 2026Lorenzo Mur-Labadia, Matthew Muckley, Amir Bar +6Self-Supervised Visual Representation LearningVisual Representation Learning

    FAIR at Meta · Universidad de Zaragoza

  23. Human-like Object Grouping in Self-supervised Vision Transformers

    Mar 14, 2026Hossein Adeli, Seoyoung Ahn, Andrew Luo +3Object-Centric Representation LearningSelf-Supervised Visual Representation Learning

    Zuckerman Mind Brain Behavior Institute, Columbia University, New York · Department of Social Science and AI, Hankuk University of Foreign Studies, Seoul · University of Hong Kong, Hong Kong +2

  24. Sat-JEPA-Diff: Bridging Self-Supervised Learning and Generative Diffusion for Remote Sensing

    Mar 14, 2026Kursat Komurcu, Linas PetkeviciusSpatiotemporal ForecastingDiffusion-Based Image Generation

    Vilnius University, Faculty of Mathematics and Informatics, Institute of Computer Science, Artificial Intelligence Methods Lab, Vilnius, LT-03225, Lithuania

  25. Towards Stable Self-Supervised Object Representations in Unconstrained Egocentric Video

    Mar 14, 2026Yuting Tan, Xilong Cheng, Yunxiao Qin +2Unsupervised Visual Concept DiscoverySelf-Supervised Visual Representation Learning

    Communication University of China, Beijing, China · State Key Laboratory of Media Convergence and Communication, Beijing, China · The Chinese University of Hong Kong, Shenzhen

  26. Pixel-level Scene Understanding in One Token: Visual States Need What-is-Where Composition

    Mar 14, 2026Seokmin Lee, Yunghee Lee, Byeonghyun Pak +1State Representation LearningSelf-Supervised Visual Representation Learning

    Agency for Defense Development

  27. SHAMISA: SHAped Modeling of Implicit Structural Associations for Self-supervised No-Reference Image Quality Assessment

    Mar 14, 2026Mahdi Naseri, Zhou WangImage Quality AssessmentSelf-Supervised Learning

    Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada

  28. Maximizing Incremental Information Entropy for Contrastive Learning

    Mar 13, 2026Jiansong Zhang, Zhuoqin Yang, Xu Wu +3Contrastive LearningSelf-Supervised Visual Representation Learning

    School of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China · Computer Vision Institute, School of Artificial Intelligence, Shenzhen University, Shenzhen, China · School of Engineering, Huaqiao University, Quanzhou, China +1

  29. Zero-Shot Cross-City Generalization in End-to-End Autonomous Driving: Self-Supervised versus Supervised Representations

    Mar 12, 2026Fatemeh Naeinian, Ali Hamza, Haoran Zhu +1End-to-End Autonomous DrivingCross-Domain Generalization

    Department of Electrical and Computer Engineering, NYU Tandon School of Engineering, Brooklyn, NY, USA.

  30. Contrastive learning-based video quality assessment-jointed video vision transformer for video recognition

    Mar 11, 2026Jian Sun, Mohammad H. MahoorVideo Quality AssessmentVision Transformer

    Department Of Computer Science, University of Denver, 2155 E Wesley Ave, Denver, 80210, CO, United States of America. · Department Of Electrical and Computer Engineering, University of Denver, 2155 E Wesley Ave, Denver, 80210, CO, United States of America.