text: for the possible confounding factor of model architec- 
  ture by evaluating all models using either a ViT-L/16 
  encoder, or a Hiera-L encoder, which has a similar num- 
  ber of parameters. For the pixel prediction baselines 
  we consider VideoMAE (Tong et al., 2022; Wang et al., 
  2023a), which trains vision transformer autoencoders 
  exclusively on video, Hiera (Ryali et al., 2023), which 
  trains a hierarchical transformer autoencoder on video, 
  and OmniMAE (Girdhar et al., 2023), which trains a 
  vision transformer autoencoder on static images and 
  video simultaneously. 
  
  5 Comparison with Prior Work 
  
  In Section 5.1, we investigate the impact of feature pre- 
  diction by comparing V-JEPA with video approaches 
  that rely on pixel prediction, while using a similar ar- 
  chitecture for all baselines. Subsequently, in Section 5.2, 
  we remove the architectural constraint and report the 
  best performance across architectures for self-supervised 
  video and image pretraining approaches. Finally, we ex- 
  plore the label-efficiency of V-JEPA relative to other self- 
  supervised video pretraining approaches in Section 5.3. 
  We further detail the evaluation setup in Appendix D. 