text: 5.3 Label-efficiency 
  
  We examine the label-efficiency of V-JEPA compared to 
  other self-supervised video models by measuring the abil- 
  ity of the pretrained backbones to adapt to downstream 
  tasks with few labels. Specifically, we investigate the 
  performance of the frozen models on Kinetics-400 and 
  Something-Something-v2 as we vary the percentage of 
  labeled examples from each dataset available for training 
  the attentive probe. We train the probes in several low- 
  shot settings: using either 5% of the train set, 10%, or 
  50%, and take 3 random splits in each setting to obtain 
  more robust metrics, resulting in 9 different evaluation 
  experiments for each model. Table 7 reports the mean 
  performances and standard deviation using the K400 
  and SSv2 validation sets. 
  
  Given a masked video, we use the V-JEPA pretrained 
  models to predict the representations of the missing 
  regions, and then use the decoder to project the rep- 
  resentations to pixel space. Figure 6b shows decoder 
  outputs for various random seeds. Qualities that are 
  common across samples represent information that is 
  contained in the predictor representation. 
  
  We find V-JEPA to be more label-efficient than other 
  self-supervised video models: decreasing the available 
  number of labeled examples for training the attentive 
  probe results in an increase in the performance gap 
  between V-JEPA and the other models. In particular, 
  the performance of the largest V-JEPA model on K400 
  drops by 12% to 68.2% top-1 when we reduce the number 
  of labeled examples by a factor of 10 
  (from roughly 
  287 examples per class to 29 examples per class). By 
  contrast, VideoMAEv2 drops by 30% to 37.0% top-1, 
  VideoMAE drops by 15.9% to 62.3% top-1, and MVD 
  drops by 14.6% to 62.6% top-1. 