text: Table 5 examines both frozen evaluation with an atten- 
  tive probe on downstream video and image tasks, as well 
  as end-to-end fine-tuning. In frozen evaluation, V-JEPA 
  outperforms the baselines on all downstream tasks, ex- 
  cept ImageNet, where we achieve 74.8% compared to 
  75.1% of an OmniMAE model trained directly on Im- 
  
  5.1 Comparison with Pixel Prediction 
  
  To investigate the effectiveness of feature prediction pre- 
  training, we first compare V-JEPA to video masked mod- 
  eling models relying on a pixel prediction loss. We control 
  
  7 