text: Table 3 Average Pooling vs. Adaptive Pooling. We pool the 
  feature map output by the frozen V-JEPA encoder using 
  an attentive probe, which is then fed into a linear classifier 
  for downstream supervised tasks (K400 and SSv2). We 
  evaluate two pooling strategies: 1) average pooling (Avg.), 
  and attentive pooling (Att.). Results are reported using 
  a single center view. Using adaptive pooling with a cross- 
  attention layer leads to improvements of +17.3 points on 
  K400 and +16.1 points on SSv2. 
  
  Table 4 Ablating Prediction Task. Models are ViT-L/16 
  networks pretrained on K710 and SSv2 and evaluated with 
  an attentive probe using a single center view. The region x is 
  sampled by masking spatio-temporal regions in the video; y is 
  the mask complement. 1) random-tube[r]: x is obtained by 
  masking a fraction r of tubes (spatial patches extended across 
  the entire temporal duration) from the video, 2) causal 
  multi-block[p]: x is restricted to the first p frames of the 
  16-frame video, which are then masked with a random set 
  of spatio-temporal blocks, 3) multi-block: x is obtained 
  by masking a random set of spatio-temporal blocks from the 
  entire video. Best performance obtained by using multiblock 
  masking. 