text: Next, in Table 6, we inspect how the V-JEPA models 
  pretrained on video stack up next to the largest state- 
  of-the-art self-supervised image and video models when 
  freezing the backbone encoder and training an attentive 
  probe on top. Our image pretrained baselines include 
  OpenCLIP (Cherti et al., 2023), DINOv2 (Oquab et al., 
  2023), and I-JEPA (Assran et al., 2023). The Open- 
  CLIP model is trained with a contrastive image-text 
  alignment objective, DINOv2 and I-JEPA are trained 
  with self-supervision. These models are known to excel 
  in their frozen-evaluation performance (Oquab et al., 
  2023); i.e., their ability to produce visual features that 
  can be applied to many downstream tasks simultane- 
  ously, without end-to-end fine-tuning, and thus pro- 
  vide highly competitive baselines. Our video pretrained 
  baselines include VideoMAE (Tong et al., 2022), Omni- 
  MAE (Girdhar et al., 2023), Hiera (Ryali et al., 2023), 
  VideoMAEv2 (Wang et al., 2023a), and MVD (Wang 
  et al., 2023b). The OpenCLIP, DINOv2 and Video- 
  MAEv2 models are parameterized as Giant/Gigantic 
  vision transformer architectures containing over 1B pa- 
  rameters trained on large-scale image or video datasets. 