text: Figure 4 SSv2 fine-tuning performance vs. Samples Seen. We 
  report SSv2 fine-tuning for V-JEPA and pixel-reconstruction 
  baselines using a ViT-L/16 or Hiera-L architecture. V-JEPA 
  outperforms all pixel-reconstruction methods using a ViT- 
  L/16 and matches the Hiera-L performance while seeing 
  significantly less samples during pretraining. 
  
  Figure 5 SSv2 frozen-evaluation performance vs. Pretraining 
  Time. Wallclock times for all methods are measured on a 
  single GPU with a batch size of 10 clips, using the official 
  codebases for VideoMAE and VideoMAEv2, and linearly 
  extrapolated assuming a global batch size of 2400 samples. 
  However, note that the SSv2 accuracies of video pixel pre- 
  diction methods are actually obtained with small batch sizes 
  and significantly longer training schedules. V-JEPA out- 
  performs pixel-reconstruction methods while training signifi- 
  cantly faster.  ageNet; hence, V-JEPA achieves comparable ImageNet 
  performance despite only pretraining on video. 