text: computed from another part of the video, x. The pre- 
  dictor network Pϕ( 
  ), which maps the representation of 
  · 
  x to the representation of y, is trained simultaneously 
  with the encoder, and is provided specification of the 
  spatio-temporal positions of y through the conditioning 
  variable z 
  
  Feature Prediction versus Pixel Reconstruction. 
  Approaches that predict in pixel space must dedicate 
  significant model capacity and compute to capture all 
  the low-level detail in the visual input. By contrast, ap- 
  proaches that predict in latent space have the flexibility 
  to eliminate irrelevant or unpredictable pixel-level details 
  from the target representation (Vondrick et al., 2016). 
  Predicting in representation space has been shown to 
  lead to versatile representations that perform well across 
  many downstream tasks through linear probing or low- 
  shot adaptation (Assran et al., 2023; Oquab et al., 2023; 
  Assran et al., 2022), while demonstrating an efficiency 
  gain during pretraining compared to pixel level recon- 
  struction (Assran et al., 2023; Baevski et al., 2022b,a). 
  The works of Baevski et al. (2022a,b) additionally show 
  that predicting in representation space results in compet- 
  itive end-to-end fine-tuning performance in the image, 
  audio and text domains. In this work, we extend these 
  findings to the video modality. 
  
  ∆y. 
  ← 
  Naively implementing the objective using the regression 