Cluster and Predict Latents Patches for Improved Masked Image Modeling
Authors: name: Timothée Darcet, name: Federico Baldassarre, name: Maxime Oquab, name: Julien Mairal, name: Piotr Bojanowski
Organization: AI at Meta

Masked Image Modeling (MIM) offers a promising approach to self-supervised
representation learning, however existing MIM models still lag behind the
state-of-the-art. In this paper, we systematically analyze target
representations, loss functions, and architectures, to introduce CAPI - a novel
pure-MIM framework that relies on the prediction of latent clusterings. Our
approach leverages a clustering-based loss, which is stable to train, and
exhibits promising scaling properties. Our ViT-L backbone, CAPI, achieves 83.8%
accuracy on ImageNet and 32.1% mIoU on ADE20K with simple linear probes,
substantially outperforming previous MIM methods and approaching the
performance of the current state-of-the-art, DINOv2. We release all our code
and models.