Joint-embedding predictive architectures
Mahmoud Assran
Coauthor of I-JEPA and V-JEPA 2, learning predictive representations from images and video.
- Tracked papers
- 5
- Past year
- 2
- Editor picks
- 1
EV-JEPA: Elastic V-JEPA for Any Device and Cross-Device Inference
EV-JEPA trains a single elastic V-JEPA model once for both standalone width changes and hybrid cross-device execution. Using Transition-Aware Triplet Training (T3), it jointly optimizes a smaller standalone path, a larger standalone path, a…
RoboJEPA: Scaling Robotic Latent World Models
RoboJEPA is a JEPA-based latent world model for robotics trained on real-robot data spanning 12 embodiments. The authors report that its imagination (latent rollout) error follows a second-order power law in training compute with verified e…
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
Pretrains an action-free JEPA video model on over 1 million hours of internet video, then post-trains a latent action-conditioned world model (V-JEPA 2-AC) on under 62 hours of unlabeled robot video and uses it to plan pick-and-place with i…
Revisiting Feature Prediction for Learning Visual Representations from Video
V-JEPA trains video models solely with a feature-prediction objective (predicting masked spatio-temporal regions in representation space), with no pretrained image encoders, text, negatives or reconstruction, on 2 million public videos.
Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture
I-JEPA learns image representations by predicting the representations of several target blocks of an image from a single context block, in representation space and with no hand-crafted data augmentations.