text: layer attentive probe, which can be further improved to 
  77.9% using a two-layer attentive probe. More generally, 
  we hypothesize that the datasets used to train V-JEPA 
  and other video models are too constrained and lack the 
  visual diversity of the internet-scale pretraining data used 
  by the images models; as such, there is value in focusing 
  future work on building diverse publicly available video 
  datasets. 
  
  to 54.0% top-1 when we reduce the number of labeled 
  examples by a factor of 10 
  (from roughly 440 examples 
  per class to 48 examples per class). By contrast, Video- 
  MAEv2 drops by 26% to 28.0% top-1, VideoMAE drops 
  by 19.1% to 41.4% top-1, and MVD drops by 18.1% to 
  42.9% top-1. 
  
  × 
  
  6 Evaluating the Predictor 
  
  Next, we seek to qualitatively inspect the V-JEPA mod- 
  els. Recall that the predictor network in V-JEPA predicts 
  the representations of a masked spatio-temporal region y 
  from a visible region x, given the positional information 
  of the masked regions (see Section 3). To qualitatively in- 
  vestigate the grounding of the feature-space predictions, 
  we freeze the pretrained encoder and predictor networks 
  and train a conditional diffusion decoder to map the 
  V-JEPA predictions to interpretable pixels. Notably, the 
  decoder is only fed the representations predicted for the 
  missing regions of the video, and does not have access 
  to the unmasked regions of the video (see Figure 6a). 