text: Self-Supervised Learning from Videos 
  
  Similar to unsupervised learning from images, a family of unsupervised video representation learning approaches 
  enforces a spatio-temporal representation of a video clip to be invariant to hand-crafted spatio-temporal data 
  augmentations (Parthasarathy et al., 2022). However, one obvious insight is that the temporal ordering of visual 
  information in video can provide implicit supervision. Indeed, this insight is the key insight leveraged by many works 
  on unsupervised video learning. Towards leveraging temporal information as supervision, some approaches train a 
  visual encoder by predicting the temporal ordering of frames (Xu et al., 2019; Lee et al., 2017). Other approaches 
  seek to predict low-level motion vectors computed from optical flow (Pintea et al., 2014), or to predict mixing pixels 
  in video frames, using either a frame-interpolation objective (Kalluri et al., 2023) or a denoising autoencoder (Tong 
  et al., 2022; Feichtenhofer et al., 2022; Wang et al., 2023a). 
  
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