text: Revisiting Feature Prediction for Learning Visual 
  Representations from Video 
  Adrien Bardes1,2,3, Quentin Garrido1,4, Jean Ponce3,5,6, Xinlei Chen1, Michael Rabbat1, Yann LeCun1,5,6, 
  Mahmoud Assran1,†, Nicolas Ballas1,† 
  1FAIR at Meta, 2Inria, 3École normale supérieure, CNRS, PSL Research University, 4Univ. Gustave Eiffel, 
  CNRS, LIGM, 5Courant Institute, New York University, 6Center for Data Science, New York University 
  †Joint last author 
  
  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 feature prediction objective, without 
  the use of pretrained image encoders, text, negative examples, reconstruction, or other sources of supervision. 
  The models are trained on 2 million videos collected from public datasets and are evaluated on downstream 
  image and video tasks. Our results show that learning by predicting video features leads to versatile visual 
  representations that perform well on both motion and appearance-based tasks, without adaption of the 
  model’s parameters; e.g., using a frozen backbone. Our largest model, a ViT-H/16 trained only on videos, 
  obtains 81.9% on Kinetics-400, 72.2% on Something-Something-v2, and 77.9% on ImageNet1K. 