287 citations · 599 across the 16 of their papers we have counts for
11 papers · 1 filter
MJEPA: A Simple and Scalable Joint-Embedding Predictive Architecture for Audio-Visual Learning
Revant Teotia, Adrien Bardes, Michael Rabbat +3
Self-supervised learning from large-scale video data has emerged as a dominant paradigm for visual representation learning. Since audio and visual streams naturally co-occur in vid…
V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning
Lorenzo Mur-Labadia, Matthew Muckley, Amir Bar +6
We present V-JEPA 2.1, a family of self-supervised models that learn dense, high-quality visual representations for both images and videos while retaining strong global scene under…
Intuitive physics understanding emerges from self-supervised pretraining on natural videos
Quentin Garrido, Nicolas Ballas, Mahmoud Assran +5
We investigate the emergence of intuitive physics understanding in general-purpose deep neural network models trained to predict masked regions in natural videos. Leveraging the vi…
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…
Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks
Micah Goldblum, Hossein Souri, Renkun Ni +10
Neural network based computer vision systems are typically built on a backbone, a pretrained or randomly initialized feature extractor. Several years ago, the default option was an…
MC-JEPA: A Joint-Embedding Predictive Architecture for Self-Supervised Learning of Motion and Content Features
Adrien Bardes, Jean Ponce, Yann LeCun
Self-supervised learning of visual representations has been focusing on learning content features, which do not capture object motion or location, and focus on identifying and diff…
No Free Lunch in Self Supervised Representation Learning
Ihab Bendidi, Adrien Bardes, Ethan Cohen +3
Self-supervised representation learning in computer vision relies heavily on hand-crafted image transformations to learn meaningful and invariant features. However few extensive ex…
VICRegL: Self-Supervised Learning of Local Visual Features
Adrien Bardes, Jean Ponce, Yann LeCun
Most recent self-supervised methods for learning image representations focus on either producing a global feature with invariance properties, or producing a set of local features.…
Bag of Image Patch Embedding Behind the Success of Self-Supervised Learning
Yubei Chen, Adrien Bardes, Zengyi Li +1
Self-supervised learning (SSL) has recently achieved tremendous empirical advancements in learning image representation. However, our understanding of the principle behind learning…
VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
Adrien Bardes, Jean Ponce, Yann LeCun
Recent self-supervised methods for image representation learning are based on maximizing the agreement between embedding vectors from different views of the same image. A trivial s…