text: puts x and y correspond to masked regions of a video, we 
  apply the video masks by simply dropping a subset of the 
  tokens. We apply masking at the input of the x-encoder, 
  and at the output of the y-encoder to construct contex- 
  tualized targets (Baevski et al., 2022b). The encoder is 
  parameterized using standard ViT networks, while the 
  predictor is a narrow transformer implemented using 
  12 blocks with an embedding dimension of 384. Taking 
  inspiration from masked autoencoders (He et al., 2021), 
  our predictor takes as input the sequence of embeddings 
  produced by the x-encoder as well as a sequence of learn- 
  able mask tokens with positional embeddings indicating 
  the spatio-temporal positions of the y tokens. The out- 
  put of the predictor is an embedding vector for each 
  mask token; see Figure 3 and refer to Appendix B for 
  more details. 
  
  3.2 Prediction Task: Predicting y from x 
  The feature prediction task is based on a masked mod- 
  eling formulation (He et al., 2021; Tong et al., 2022); 
  i.e., regions x and y from the video are sampled using 
  masking. To sample y from a video, we sample several 
  (possibly overlapping) spatially continuous blocks with 
  various aspect ratios and repeat the spatial blocks across 
  the entire temporal dimension of the video; x is taken to 
  be the complement. Masking a large continuous block 
  that covers the full temporal dimension limits informa- 
  tion leakage due to the spatial and temporal redundancy 
  of videos, and results in a harder prediction task (Tong 
  et al., 2022). 