text: V-JEPA. We sample both a video clip, and a video mask in each iteration. We denote a video clip represented as 
  a 1D token sequence of length L = 1568 by xL = (x1, . . . , xL). Similarly, given a mask of M < L patches, leaving 
  M patches unmasked, we denote the indices of masked patches by (i1, . . . , iM ) and its complement (the 
  N = L 
  indices of unmasked patches) by (j1, . . . , jN ). 
  − 
  
  Computing the x-representations. To compute the V-JEPA loss, we first produce the x-representations by masking 
  the video clip and feeding it into the x-encoder; we denote the masked video by xN = (xj1 , . . . , xjN ). Applying the x- 
  ) to the masked clip gives a sequence of patch representations, denoted as zN = Eθ(xN ) = (zj1, . . . , zjN ). 
  encoder Eθ( 
  · 
  
  Predicting the target. Next, the V-JEPA predictor network Pϕ( 
  ) takes as input the tokens produced by the 
  · 
  x-encoder and predicts the missing regions in the video clip, which are specified by a set of learnable mask tokens. 
  Specifically, the mask tokens are parameterized as the sum of a shared learnable vector and an absolute 3D 
  sin-cos positional embedding, denoted by mM = (mi1 , . . . , miM ). The output of the predictor is thus given by, 
  ˆsM = Pϕ(zN , mM ) = (ˆsi1 , . . . , ˆsiM ), corresponding to a d-dimensional output for each of the M masked patches. 
  
  , 
  · 