text: Table 7 Low-Shot Frozen Evaluation. Comparing V-JEPA to other video models in frozen evaluation 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. We report 
  the mean performances and standard deviation using the K400 and SSv2 validation sets. V-JEPA is more label-efficient than 
  other models; specifically, decreasing the available number of labeled examples from each class increases the performance gap 
  between V-JEPA and the baselines. 