text: To that end, we pretrain a family of V-JEPA models 
  on a dataset of 2 million videos collected from pub- 
  licly available datasets by combining a masked modeling 
  prediction task with a joint-embedding predictive ar- 
  chitecture (see Figure 2). We measure performance on 
  several downstream image and video tasks, using both 
  frozen evaluation and end-to-end fine-tuning. Our find- 
  ings suggest that feature prediction can indeed serve as 
  an effective stand-alone objective for unsupervised learn- 
  ing from video, while using significantly shorter training 
  schedules than pixel prediction methods. Specifically: 
  
  variance and explores feature prediction using masked 
  modeling. 
  
  • Feature prediction leads to versatile visual repre- 
  sentations that perform well across downstream 
  image and video tasks without adaption of the 
  i.e., using a frozen backbone. 
  model’s weights; 
  V-JEPA achieves the best performance among 
  methods we consider (+6% accuracy) on the 
  SomethingSomething-v2 task, which requires fine- 
  V-JEPA is 
  grained temporal understanding. 
  also competitive on tasks like Kinetics400, where 
  appearance-based features are sufficient and hence 
  state-of-the-art image models such as DINOv2 excel 
  (Figure 1 and Table 6). 