text: Comparison with image models. On tasks that re- 
  quire a fine-grained understanding of motion (Something- 
  Something-v2), the V-JEPA models provide a major im- 
  provement (over +21 points) compared to large-scale 
  image baselines, such as DINOv2, OpenCLIP, and I- 
  JEPA. Self-supervised pretraining from videos allows to 
  model dynamic concepts that are not easily learned from 
  static image datasets. Similarly, we observe that the 
  V-JEPA models outperform image-based pretraining on 
  action localization. 
  
  On Kinetics-400, we find image models to perform well; 
  e.g., while DINOv2 (Oquab et al., 2023) previously re- 
  ported 78.4% on K400 with a linear probe, we improve 
  the frozen evaluation of the g/14 model to 83.4% by 
  using an attentive probe. In this case, our H/16 model 
  achieves 82.0% top-1 accuracy. It is worth noting that 
  the label for many Kinetics videos can be inferred using 
  appearance-based cues, without requiring an understand- 
  ing of motion (Sevilla-Lara et al., 2021). 