text: Predictive Features. Going beyond local invariance, 
  a family of works trains a predictor network to map the 
  representation of a frame or clip at one time-step to a 
  distinct representation at another time-step. Srivastava 
  et al. (2015); Vondrick et al. (2016); Wang et al. (2023b) 
  train such a video feature predictor network on top of 
  a frozen pretrained image or video encoder. Unfreezing 
  the target feature extractor, several methods train the 
  video encoder and the predictor network simultaneously, 
  while preventing collapse by using a supervised action 
  forecasting loss (Girdhar and Grauman, 2021), or by 
  using the representations of distant clips as negative 
  samples in a contrastive loss (Han et al., 2019, 2020; 
  Tan et al., 2023), often focusing on small convolutional 
  encoders (Han et al., 2019, 2020). The idea of learning a 
  representation by predicting missing information in fea- 
  ture space is also core to the joint-embedding predictive 
  architecture (JEPA) (LeCun, 2022), which combines a 
  siamese encoder with a predictor network. JEPAs have 
  been successfully instantiated in several modalities, such 
  as with audio data (Baevski et al., 2022b) and image 
  data (Zhou et al., 2021; Oquab et al., 2023; Assran et al., 
  2023). In this work, we extend this paradigm to video 
  data by leveraging recent advances in self-supervised 
  learning. 