text: Table 5 Comparison with Pixel Prediction Methods. We compare V-JEPA with OmniMAE (Girdhar et al., 2023), Video- 
  MAE (Tong et al., 2022), and Hiera (Ryali et al., 2023), which leverage a pixel-reconstruction loss. All models are trained using 
  a ViT-L architecture or a comparable Hiera-L. We evaluate the approaches on downstream image tasks (IN1K, Places205, 
  iNat201) and video tasks (K400, SSv2, AVA) in both frozen evaluation (with a frozen backbone), and end-to-end fine-tuning. 
  All models are evaluated at resolution 224. On K400 and SSv2 we follow the standard practice of reporting accuracy from 
  several spatial and temporal views from the video. In frozen evaluation, V-JEPA outperforms the baselines on all downstream 
  tasks, except ImageNet, where the model achieves 74.8% compared to 75.1% of an OmniMAE model trained directly on 
  ImageNet. V-JEPA also achieves the best fine-tuning performance amongs all ViT-L models and matches the Hiera-L on 
  SSv2. The V-JEPA results are achieved while processing significantly fewer examples during pretraining. 