MR-JEPA: A General Purpose Video Foundation Model for Cardiac MRI

arXiv:2608.30975 · cs.CV, cs.AI · Submitted 2026-08-31 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-08-31

Updated: 2026-08-31

Terminology

• Foundation Model — 25× in this paper · explained in 3 episodes
• AUC — 11× in this paper · explained in 2 episodes
• Downstream Tasks — 7× in this paper · explained in 2 episodes
• Embeddings — 6× in this paper · explained in 2 episodes
• Foundation Models — 6× in this paper · explained in 7 episodes
• Self-Supervised Learning — 5× in this paper · explained in 7 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Contrastive Learning — 3× in this paper · explained in 5 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Representation Learning — 3× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Linear Probing — 2× in this paper · explained in 2 episodes
• Systematic Bias — 2× in this paper · explained in 3 episodes
• APEX — 1× in this paper · explained in 1 episode
• Ablation — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Joint-Embedding Predictive Architectures — 1× in this paper
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Self-Supervised Learning (SSL) — 1× in this paper · explained in 4 episodes
• Self-distillation — 1× in this paper · explained in 6 episodes
• Technical University of Munich — 1× in this paper
• Tokenization — 1× in this paper · explained in 2 episodes
• Transferability — 1× in this paper · explained in 4 episodes

Sources

• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
• LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics
• Revisiting Feature Prediction for Learning Visual Representations from Video
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• A versatile foundation model for cine cardiac magnetic resonance image analysis tasks
• Decoupled Weight Decay Regularization
• DINOv2: Learning Robust Visual Features without Supervision
• DINOv3

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models