text: stream tasks requiring fine-grained motion understand- 
  ing, while large-scale image models trained on internet 
  scale datasets fall short on such tasks. Finally, we em- 
  pirically observed that V-JEPA models are label-efficient 
  learners, and exhibit good performance on downstream 
  tasks, even when only few labeled examples are available. 
  
  7 Conclusion 
  
  In this work, we explored the effectiveness of feature 
  prediction as a stand-alone objective for unsupervised 
  learning from video and introduced V-JEPA, a collection 
  of vision models trained solely using a self-supervised 
  feature prediction objective. The V-JEPA models demon- 
  strate the ability to solve various downstream image and 
  video tasks without adaption of the model parameters, 
  and outperform previous video representation learning 
  approaches in frozen evaluation on action recognition, 
  spatio-temporal action detection, and image classifica- 
  tion tasks. Additionally, we show that pretraining V- 
  JEPA on videos is particularly effective for solving down- 
  Hassan Akbari, Liangzhe Yuan, Rui Qian, Wei-Hong Chuang, 
  Shih-Fu Chang, Yin Cui, and Boqing Gong. Vatt: Trans- 
  formers for multimodal self-supervised learning from raw 
  video, audio and text. Advances in Neural Information 
  Processing Systems, 34:24206–24221, 2021. 