  This paper explores feature prediction as a stand-alone objective for
unsupervised learning from video and introduces V-JEPA, a collection of vision
models trained solely using a feature prediction objective, without the use of
pretrained image encoders, text, negative examples, reconstruction, or other
sources of supervision. The models are trained on 2 million videos collected
from public datasets and are evaluated on downstream image and video tasks. Our
results show that learning by predicting video features leads to versatile
visual representations that perform well on both motion and appearance-based
tasks, without adaption of the model's parameters; e.g., using a frozen
backbone. Our largest model, a ViT-H/16 trained only on videos, obtains 81.9%
on Kinetics-400, 72.2% on Something-Something-v2, and 77.9% on ImageNet1K.
