Multimodal SSL

SSL: Self-Supervised Learning

Latest papers

14 papers5 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

SleepPairNet: Learning Multimodal Pediatric Sleep Representations with Random-Pair Cross-Attention
Oct 19, 2026Saurav Raj Pandey, Harlin LeeMultimodal Pretraining Cross-Modal Representation Learning
Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA · School of Data and Information Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
MM-LeJEPA: Correlated Gaussian Regularization for Multimodal Joint-Embedding Predictive Architectures
Oct 17, 2026Haowen Bai, Rui Gong$, Meichen Liu +3Multimodal SSL Joint-Embedding Predictive Architecture
Nanyang Technological University · Hyundai Motor Group Innovation Center Singapore (HMGICS)
HRIL: Learning Multimodal Synergy via Higher-Order Tensor Modeling
Oct 8, 2026Qun Dai, Liangjian Wen, Jiang Duan +7Multimodal SSL Multimodal Learning
School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics · Artificial Intelligence and Digital Finance Key Laboratory of Sichuan Province · Chengdu Everimaging Science and Technology Co., Ltd +5
LeAVJEPA: A Minimalist Architecture for Audio-Visual Self-Supervised Learning
Oct 5, 2026Benjamin Robson, Santeri Mentu, Wenshuai Zhao +1Audio-Visual Representation Learning Joint-Embedding Predictive Architecture
ELLIS Institute Finland Aalto University
On the Geometry of Multimodal Saturation: Riemannian VICReg
Oct 5, 2026Nessim Ben Abbes, Duc Han Le, Sabri Mtibaa +1Self-Supervised Learning Geometric Representation Learning
Protectline · Télécom Paris
SynCo: Learning Cross-Modal Synergy by Contrasting Interaction Residuals
Sep 26, 2026Yavuz Yarici, Ghassan AlRegibCross-Modal Representation Learning Multimodal Contrastive Learning
OLIVES, CSIP, School of ECE Georgia Institute of Technology Atlanta, GA, USA
Synergistic Information Disentanglement for Omni-modal Slide Representation Learning in Computational Pathology
Sep 2, 2026Mingxin Liu, Chengfei Cai, Anwen Lu +5Multimodal SSL Medical Image Representation Learning
Jiangsu Key Laboratory of Intelligent Medical Image Computing, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China · Jiangsu Key Laboratory of Intelligent Drug Screening and Repositioning, College of Information Engineering, Taizhou University, Taizhou, China · College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
MUST-PET: MUltimodal Self-supervised learning across Tracers for whole-body PET/CT-based lesion segmentation
Aug 20, 2026Bashirul Azam Biswas, Amartya Bhattacharya, Biratal Raj Wagle +3Multimodal SSL PET/CT Tumor Segmentation
Department of Biomedical Data Science Geisel School of Medicine at Dartmouth Hanover, NH 03755, USA · Radiology Dartmouth Hitchcock Medical Center Lebanon, NH 03766, USA · Radiation Oncology Dartmouth Hitchcock Medical Center Lebanon, NH 03766, USA
OctoSense: Self-Supervised Learning for Multimodal Robot Perception
Jun 25, 2026Anthony Bisulco, Jeremy Wang, Kostas Daniilidis +2Autonomous Driving Datasets Masked Autoencoders
1General, Robotics, Automation, Sensing and Perception (GRASP) Laboratory, University of Pennsylvania · Computer Science Department, Brown University
Cross-Modal-Domain Generalization Through Semantically Aligned Discrete Representations
May 12, 2026Souptik Sen, Raneen Younis, Zahra AhmadiMultimodal Learning Representation Learning
Peter L. Reichertz Institute for Medical Informatics, Hannover Medical School, Germany · Lower Saxony Center for AI and Causal Methods in Medicine (CAIMed), Hannover, Germany
SSL-R1: Self-Supervised Visual Reinforcement Post-Training for Multimodal Large Language Models
Apr 22, 2026Jiahao Xie, Alessio Tonioni, Nathalie Rauschmayr +2Multimodal Large Language Models Reinforcement Learning with Verifiable Rewards
Max Planck Institute for Informatics, SIC · VIA Research Center · Google
Le MuMo JEPA: Multi-Modal Self-Supervised Representation Learning with Learnable Fusion Tokens
Mar 25, 2026Ciem Cornelissen, Sam Leroux, Pieter SimoensMultimodal SSL Multimodal Learning
IDLab, Department of Information Technology, Ghent University - imec, Belgium
PrismSSL: One Interface, Many Modalities; A Single-Interface Library for Multimodal Self-Supervised Learning
Nov 21, 2025Melika Shirian, Kianoosh Vadaei, Kian Majlessi +3Multimodal SSL Self-Supervised Learning
dept. Computer Engineering University of Isfahan Isfahan, Iran · dept. Computer Engineering University of Texas at Dallas Isfahan, Iran · dept. Artificial Intelligence University of Isfahan Isfahan, Iran
FairSSL: Fair Multimodal Self-Supervised Learning
Aug 22, 2025Jiaee Cheong, Abtin Mogharabin, Paul Liang +2Cross-Modal Learning Representation Learning
Harvard University · University of Cambridge · Middle East Technical University +1

Oct 19, 2026J/K move · Enter open · S save

SleepPairNet: Learning Multimodal Pediatric Sleep Representations with Random-Pair Cross-Attention

Saurav Raj Pandey, Harlin Lee

Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA · School of Data and Information Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Multimodal Pretraining Cross-Modal Representation Learning Multimodal SSL Cross-Modal Attention

Open paper AskPDF arXiv

Analyzing physiological sleep signals is important for assessing sleep quality and detecting sleep-related disorders. Learning inter-modal relationships across physiological signals can provide richer representations for downstream sleep analysis. We introduce SleepPairNet, a multimodal self-supervised learning (SSL) framework that learns pairwise interactions between physiological signals through cross-attention. We propose a random modality-pair selection strategy that exposes the model to diverse signal pairs during pretraining, enabling flexibility across modality combinations at test time. Pretrained on the NCH dataset, SleepPairNet outperforms the evaluated SSL baselines on all respiratory tasks while remaining competitive on EEG-related tasks, and shows even stronger results when transferred to the external CHAT dataset.