ing eﬀorts towards these foundation models focus on text-guided pretraining, i.e., using a form of textual
supervision to guide the training of the features (Joulin et al., 2016; Mahajan et al., 2018; Radford et al.,
2021). This form of text-guided pretraining limits the information that can be retained about the image
since captions only approximate the rich information in images, and complex pixel-level information may
All the authors are aﬃliated to Meta, except Julien Mairal who is aﬃliated to Inria. Timothée Darcet and Pierre Fernandez
have a co-aﬃliation with Inria. Théo Moutakanni has a co-aﬃliation with Université Paris Saclay. Alaaeldin El-Nouby has a
co-aﬃliation with Inria and ENS-PSL. Correspondence: {qas, timdarcet, theomoutakanni, ajoulin, bojanowski}@meta.com
DINOv2- Learning Robust Visual Features without Supervision