11 citations · 26 across the 15 of their papers we have counts for
4 papers · 1 filter
VEDIT: Latent Prediction Architecture For Procedural Video Representation Learning
Han Lin, Tushar Nagarajan, Nicolas Ballas +4
Procedural video representation learning is an active research area where the objective is to learn an agent which can anticipate and forecast the future given the present video in…
Modeling Caption Diversity in Contrastive Vision-Language Pretraining
Samuel Lavoie, Polina Kirichenko, Mark Ibrahim +4
There are a thousand ways to caption an image. Contrastive Language Pretraining (CLIP) on the other hand, works by mapping an image and its caption to a single vector -- limiting h…
Revisiting Feature Prediction for Learning Visual Representations from Video
Adrien Bardes, Quentin Garrido, Jean Ponce +5
This paper explores feature prediction as a stand-alone objective for unsupervised learning from video and introduces V-JEPA, a collection of vision models trained solely using a f…
Learning and Leveraging World Models in Visual Representation Learning
Quentin Garrido, Mahmoud Assran, Nicolas Ballas +3
Joint-Embedding Predictive Architecture (JEPA) has emerged as a promising self-supervised approach that learns by leveraging a world model. While previously limited to predicting m…