text: Weakly-Supervised Learning from Static Images 
  
  One family of approaches for learning visual perception from static images trains a visual encoder to predict the 
  representations of text captions often found accompanying images from the Web, as in CLIP (Radford et al., 2021) or 
  CoCa (Yu et al., 2022). The largest open source CLIP model to date, numbering 2B parameters and trained on over 
  2B web-scraped images (Cherti et al., 2023), demonstrates impressive performance on a wide range of downstream 
  image and video tasks. Notably, this is achieved using only the light-weight adaptation of task-specific heads, also 
  referred to as frozen-evaluation, and does not require expensive end-to-end fine-tuning of the pretrained model. 