text: Table 14 Temporal Coverage on Kinetics-400. We evaluate the effect of temporal coverage on K400. We train an attentive 
  probe on K400 using either 1 clip (≈ 2 seconds of a video) or 8 clips (≈ 16 seconds of a video). To sample N clips, we first 
  divide a video in N equal-length temporal segments and sample one clip at random per segment. The video encoder processes 
  each clip in parallel and all the encoder output tokens are concatenated at the input of the attentive probe. Increasing the 
  temporal coverage from 1 clip per video to 8 clips significantly improves the performance for both our VideoMAE baseline 
  and V-JEPA. 
  
  Method 
  VideoMAE ViT-L/16 
  ViT-L/16 
  V-JEPA 
  
  Arch.  1 Clip  8 Clips 
  
  69.4 
  73.7  77.8 
  80.9 
  
  Table 15 Finetuning results. We evaluate a V-JEPA model with the finetuning protocol on the K400 and SSv2 datasets 
  using 16 frames per clip and multi-view fusion (5×3 or 2×3) for inference. The #Samples Seen entry corresponds to the 
  number of video clips processed during pretraining, which is larger than the size of the pretraining dataset for multi-epoch 
  training. We compare V-JEPA with different video self-supervised learning approaches. We report the VideoMAEv2 results 
  without instruction-turning for consistency with the other approaches. V-JEPA obtains competitive performance using the 
  finetuning protocol. 