text: 1 Introduction 
  
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  Humans possess the remarkable ability to map low-level 
  signals originating from the retina into a semantic spatio- 
  temporal understanding of the world; synthesizing no- 
  tions such as objects and global motion (Spelke et al., 
  1995). A long-standing goal of the machine learning 
  community is to identify the principles or objectives that 
  may guide such unsupervised learning in humans (Field, 
  1994; Berkes and Wiskott, 2005; Hinton, 1989). One 
  related hypothesis is based on the predictive feature 
  principle (Rao and Ballard, 1999), which posits that 
  representations of temporally adjacent sensory stimuli 
  should be predictive of each other. 
  
  In this work, we revisit feature prediction as a stand- 
  alone objective for unsupervised learning of visual repre- 
  sentations from video. Numerous advances in the field — 
  such as the standard use of transformer architectures in 
  vision (Dosovitskiy et al., 2020), the maturing of masked 
  autoencoding frameworks (Xie et al., 2021; Bao et al., 
  2021; He et al., 2021), query-based feature pooling (Chen 
  et al., 2022), joint-embedding predictive architectures 
  (JEPA) (LeCun, 2022; Assran et al., 2023; Baevski et al., 
  2022b), and larger datasets — form a unique arsenal of 
  tools, which we integrate in a modern and conceptually 
  simple method, the video joint-embedding predictive ar- 
  chitecture or V-JEPA, which is based solely on feature 
  prediction, without using pretrained image encoders, 
  text, negative examples, human annotations, or pixel- 