text: 4.4 Prediction Task: Predicting y from x 
  
  We conduct an ablation on the masking strategy used in 
  V-JEPA pretraining. We examine the following masking 
  strategies: random-tube[r] in which x is obtained by 
  removing a random fraction r of tubes (spatial patches 
  extended across the entire temporal duration) from the 
  video, causal multi-block[p] in which 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, 
  and multi-block in which x obtained by masking a ran- 
  dom set of spatio-temporal blocks from the entire video. 
  Spatio-temporal blocks are sampled using the parame- 
  ters described in Section 3.2; an ablation on the size and 
  quantity of masked spatio-temporal blocks is provided 
  in Appendix E.4. 
  
  4.3 Evaluation: Attentive Probing 
  
  Next we explore the feature pooling strategy for apply- 
  ing the model’s representations in downstream tasks. 
  Since the prediction objective in equation (1) is unnor- 
  malized, there is no a priori reason for the encoder to 
  yield a linearly separable subspace (Chen et al., 2020). 
  Thus, rather than using a linear operation (averaging) 
  to pool the features output of the frozen backbone, we 
  explore a learnable non-linear pooling strategy. Specifi- 
  cally, when evaluating the frozen pretrained backbone 
  on downstream tasks, we learn a cross-attention layer 
  with a learnable query token. The output of the cross- 
  attention layer is then added back to the query token 
  (residual connection), and then fed into two-layer MLP 