Self-Supervised Visual Representation Learning

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20 papers in the last four weeks, up 100% on the four weeks before. 0.2% of all new papers.

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Representation learning from OCT images
May 4, 2026Hedi Tabia, Désiré Sidibé, Nawres Khlifa +6Medical Imaging Medical Imaging Foundation Models
IBISC Univ. Evry Université Paris-Saclay · University of Tunis El Manar · ESIEE Paris - Université Gustave Eiffel +2
SignMAE: Segmentation-Driven Self-Supervised Learning for Sign Language Recognition
May 3, 2026Kunyuan Xie, Zhixi Cai, Kalin StefanovSelf-Supervised Pre-Training Sign Language Recognition
Monash University, Australia
ProtoFair: Fair Self-Supervised Contrastive Learning via Pseudo-Counterfactual Pairs
May 3, 2026Marah Halawa, Olaf HellwichRepresentation Learning Algorithmic Fairness
Technische Universität Berlin
Self-Supervised Learning of Plant Image Representations
Apr 30, 2026Ilyass Moummad, Kawtar Zaher, Hervé Goëau +3Data Augmentation Fine-Grained Image Classification
INRIA, LIRMM, Université de Montpellier, Montpellier, France · Institut National de l’Audiovisuel, Paris, France · AMAP, Université de Montpellier, CIRAD, INRAE, IRD, Montpellier, France
Self-Supervised Representation Learning via Hyperspherical Density Shaping
Apr 27, 2026Esteban Rodríguez-Betancourt, Edgar Casasola-MurilloMulti-View Learning Hyperspherical Representation Learning
Posgrado en Computación e Informática · Universidad de Costa Rica · Escuela de Ciencias de la Computación
Geometric Analysis of Self-Supervised Vision Representations for Semantic Image Retrieval
Apr 27, 2026Esteban Rodríguez-Betancourt, Edgar Casasola-MurilloNeural Representation Geometry Self-Supervised Visual Representation Learning
Posgrado en Computación e Informática Universidad de Costa Rica · Escuela de Ciencias de la Computación Universidad de Costa Rica
Complexity of Linear Regions in Self-supervised Deep ReLU Networks
Apr 27, 2026Mufhumudzi Muthivhi, Terence L. van ZylRepresentation Collapse Self-Supervised Visual Representation Learning
University of Johannesburg, Johannesburg, South Africa
Sapiens2
Apr 23, 2026Rawal Khirodkar, He Wen, Julieta Martinez +3Vision Foundation Models Human Pose Estimation
Meta Reality Labs
Trust-SSL: Additive-Residual Selective Invariance for Robust Aerial Self-Supervised Learning
Apr 23, 2026Wadii Boulila, Adel Ammar, Bilel Benjdira +1Contrastive Learning Image Corruption Robustness
Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh 11586, Saudi Arabia
Self-supervised pretraining for an iterative image size agnostic vision transformer
Apr 22, 2026Nedyalko Prisadnikov, Danda Pani Paudel, Yuqian Fu +1Self-Supervised Pre-Training Vision Transformer
INSAIT, Sofia University "St. Kliment Ohridski", Bulgaria
Geometry-Guided Self-Supervision for Ultra-Fine-Grained Recognition with Limited Data
Apr 21, 2026Shijie Wang, Yadan Luo, Zijian Wang +3Fine-Grained Image Classification Geometric Representation Learning
College of Computer Science and Engineering, Shandong University of Science and Technology, China · School of Information Technology and Electrical Engineering, The University of Queensland, Australia
Benchmarking Vision Foundation Models for Domain-Generalizable Face Anti-Spoofing
Apr 21, 2026Mika Feng, Pierre Gallin-Martel, Koichi Ito +1Vision Foundation Models Domain Generalization
Graduate School of Information Sciences, Tohoku University, Japan
Stylistic-STORM (ST-STORM) : Perceiving the Semantic Nature of Appearance
Apr 17, 2026Hamed Ouattara, Pierre Duthon, Pascal Houssam Salmane +2Disentangled Representation Learning Representation Learning
Intelligent Transportation Systems Research Team, Cerema, 8 rue Bernard Palissy, 63017 Clermont-Ferrand, France · Intelligent Transportation Systems Research Team, Cerema, 1 avenue du Colonel Roche, 31400 Toulouse, France · Clermont Auvergne INP, Universit´e Clermont Auvergne, Institut Pascal, CNRS, 4 Avenue Blaise Pascal, 63178 Clermont-Ferrand, France
Using Deep Learning Models Pretrained by Self-Supervised Learning for Protein Localization
Apr 13, 2026Ben Isselmann, Dilara Göksu, Heinz Neumann +1Supervised Fine-Tuning Self-Supervised Pre-Training
Department of Mathematics and Natural Sciences, Hochschule Darmstadt, Schoeﬀerstraße 3, Darmstadt, 64295, Hessen, Germany. · Department of Chemical Engineering and Biotechnology, Hochschule Darmstadt, Stephanstrasse 7, Darmstadt, 64295, Hessen, Germany. · European University of Technology, European Union. +1
To View Transform or Not to View Transform: NeRF-based Pre-training Perspective
Mar 30, 2026Hyeonjun Jeong, Juyeb Shin, Dongsuk Kum3D Reconstruction Autonomous Driving Perception
KAIST, Daejeon, Korea
Towards Interpretable Foundation Models for Retinal Fundus Images
Mar 19, 2026Samuel Ofosu Mensah, Camila Roa, Kerol Djoumessi +1Medical Imaging Medical Imaging Foundation Models
Hertie Institute for AI in Brain Health, University of Tübingen, Germany · Tübingen AI Center, University of Tübingen, Germany
Human-like Object Grouping in Self-supervised Vision Transformers
Mar 14, 2026Hossein Adeli, Seoyoung Ahn, Andrew Luo +3Object-Centric Representation Learning Self-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
TransMASK: Masked State Representation through Learned Transformation
Mar 5, 2026Sagar Parekh, Preston Culbertson, Dylan P. LoseyRepresentation Learning Robot Imitation Learning
Mechanical Engineering Department, Virginia Tech · Computer Science Department, Cornell University
Separators in Enhancing Autoregressive Pretraining for Vision Mamba
Mar 4, 2026Hanpeng Liu, Zidan Wang, Shuoxi Zhang +2Visual Autoregressive Models Visual SSMs
Huazhong University of Science and Technology
US-JEPA: A Joint Embedding Predictive Architecture for Ultrasound
Feb 22, 2026Ashwath Radhachandran, Vedrana Ivezić, Shreeram Athreya +2Joint-Embedding Predictive Architecture Ultrasound Imaging
Biomedical Artificial Intelligence Research Lab, University of California, Los Angeles, Los Angeles, United States of America · Medical Informatics, University of California, Los Angeles, Los Angeles, United States of America · Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, United States of America +3
Hypersolid: Emergent Vision Representations via Short-Range Repulsion
Jan 29, 2026Esteban Rodríguez-Betancourt, Edgar Casasola-MurilloUnsupervised Learning Representation Learning
Posgrado en Computación e Informática, Universidad de Costa Rica, San José, Costa Rica · Escuela de Ciencias de la Computación e Informática, Universidad de Costa Rica, San José, Costa Rica
MTV: Revisiting Multi-Task Visual Representation Learning
Jan 20, 2026Shangzhe Di, Zhonghua Zhai, Weidi XieVisual Representation Learning Self-Supervised Visual Representation Learning
SAI, Shanghai Jiao Tong University · ByteDance Seed
The Inductive Bottleneck: Data-Driven Emergence of Representational Sparsity in Vision Transformers
Dec 8, 2025Kanishk AwadhiyaRepresentation Learning Vision Transformer
Indian Institute of Technology, Delhi
Predicting upcoming visual features during eye movements yields scene representations aligned with human visual cortex
Nov 16, 2025Sushrut Thorat, Adrien Doerig, Alexander Kroner +2Predictive Coding Visual Neuroscience
Institute of Cognitive Science, Osnabrück University, Osnabrück 49069, Germany. · Lossfunk, Bengaluru 560038, India. · Department of Education and Psychology, Freie Universität, Berlin 14195, Germany. +1
Φeat: Physically Grounded Material Feature Representation
Nov 14, 2025Giuseppe Vecchio, Adrien Kaiser, Claudia Cuttano +4Representation Learning Visual Representation Learning
Adobe Research, France
Enhancing Self-Supervised Visual Representation Learning via Low-Rank Adapted LLMs
Jul 1, 2025Selim Kuzucu, Muhammad Ferjad Naeem, Anna Kukleva +2Vision Transformer Self-Supervised Visual Representation Learning
Max Planck Institute for Informatics, SIC · Google
Bringing Generative Learning to Representation Learning: Self-Supervised Transfer Learning as Distribution Matching
Feb 20, 2025Yuling Jiao, Wensen Ma, Defeng Sun +2Visual Representation Learning Distribution Matching
School of Artificial Intelligence, Wuhan University, 430072, Wuhan, China · School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China · Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China +2
SynCo: Synthetic Hard Negatives for Contrastive Visual Representation Learning
Oct 3, 2024Nikos Giakoumoglou, Tania StathakiContrastive Learning Hard Negative Mining
Imperial College London London, UK, SW7 2AZ
Learning to Visually Connect Actions and their Effects
Jan 19, 2024Paritosh Parmar, Eric Peh, Basura FernandoVideo Reasoning Self-Supervised Visual Representation Learning
Institute of High-Performance Computing, Agency for Science, Technology and Research, Singapore
Domain Knowledge-Informed Self-Supervised Representations for Workout Form Assessment
Feb 28, 2022Paritosh Parmar, Amol Gharat, Helge RhodinContrastive Learning Action Quality Assessment
University of British Columbia · FlexAI Inc.
DenseTRF: Texture-Aware Unsupervised Representation Adaptation for Surgical Scene Dense Prediction
Date pendingGuiqiu Liao, Matjaž Jogan, Daniel A. HashimotoUnsupervised Domain Adaptation Surgical Video Understanding
GRASP Laboratory, University of Pennsylvania · PCASO Laboratory, Department of Surgery, University of Pennsylvania · Department of Computer and Information Science, University of Pennsylvania

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Representation learning from OCT images

Hedi Tabia, Désiré Sidibé, Nawres Khlifa, Ahmed Tabia+5

IBISC Univ. Evry Université Paris-Saclay · University of Tunis El Manar · ESIEE Paris - Université Gustave Eiffel+2

Medical Imaging Medical Imaging Foundation Models Optical Coherence Tomography 3D Representation Learning Medical Image Representation Learning Self-Supervised Visual Representation Learning+1

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Optical Coherence Tomography (OCT) has become one of the most used imaging modality in ophthalmology. It provides high-resolution, non-invasive visualization of retinal microarchitecture. The automated analysis of OCT images through representation learning has emerged as a central research frontier. This has mainly been driven by the clinical need to process large acquisition volumes. The objective is to reduce the reliance on expert annotation, and improve diagnostic consistency across devices and populations. This survey provides a comprehensive and structured review of representation learning methods for retinal OCT image analysis. It covers the period from early deep learning approaches to the most recent developments in foundation models and vision-language systems. We organize the literature along a principled taxonomy of learning paradigms, encompassing supervised learning with CNN-based and transformer-based architectures, self-supervised and semi-supervised methods, generative approaches, as well as 3D volumetric modeling, multimodal representation learning, and large-scale pretrained foundation models. For each paradigm, we analyze the core methodological contributions, identify persistent limitations, and trace the connections between successive approaches. We further provide a structured overview of publicly available OCT datasets, discuss evaluation protocol considerations, and present a unified problem formulation that situates each learning paradigm within a common mathematical framework. Building on this analysis, we identify and discuss the most pressing open research directions emerging in the literature. This includes volumetric foundation model pretraining, uncertainty-aware representation learning, federated and privacy-preserving training, fairness and bias mitigation, concept-based interpretability,...

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