text: Figure 3 V-JEPA. Training operates on a video clip of T frames with spatial resolution H × W , flattened into a sequence 
  of L tokens. (Left to right): We first obtain the input of the x-encoder by dropping tokens from the video clip. The 
  x-encoder then processes the masked video sequence, and outputs an embedding vector for each input token. Next, the 
  outputs of the x-encoder are concatenated with a set of learnable mask tokens containing positional embeddings of the masked 
  spatio-temporal patches. The predictor network processes the combined token sequence, and outputs an embedding vector for 
  each mask token. The outputs of the predictor are then regressed to the prediction targets using an L1 loss. The prediction 
  targets correspond to the output of the y-encoder. 