text: Under the fine-tuning protocol, V-JEPA also achieves the 
  best performance of any model trained with a ViT-L/16, 
  and matches the performance of the Hiera-L on SSv2, 
  which benefits from a hierachical prior (Ryali et al., 2023). 
  The V-JEPA models achieve this result while processing 
  significantly fewer samples during pretraining (Figure 4), 
  demonstrating the efficiency of feature prediction as a 
  learning principle. 
  
  and image task with notable margin (see Table 6). Our 
  H/16 model outperforms the largest publicly available 
  VideoMAE, VideoMAEv2, OmniMAE, MVD, and Hiera 
  models by at least +5 points in motion understanding 
  (Something-Something-v2), +2 points in action recogni- 
  tion (Kinetics-400), +5 points on action detection (AVA), 
  +1 point on object recognition (ImageNet-1K), +2 points 
  in scene recognition (Places205), and +0.2 points on fine- 
  grained recognition (iNaturalist). Moreover, when com- 
  paring pretraining wallclock time in Figure 5, we see that 
  V-JEPA achieves this performance with a roughly 2 
  × 
  speedup compared to the large pixel prediction models. 
  
  5.2 Comparison with State-of-the-Art 