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arXiv · 2502.08769

Cluster and Predict Latent Patches for Improved Masked Image Modeling

Timothée Darcet·Federico Baldassarre·Maxime Oquab·Julien Mairal·Piotr Bojanowski
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Abstract

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.

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Timothée Darcet, Federico Baldassarre, Maxime Oquab, Julien Mairal, Piotr Bojanowski. 2025-02-12. Cluster and Predict Latent Patches for Improved Masked Image Modeling. https://arxiv.org/abs/2502.08769

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