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DINOv2: Learning Robust Visual Features without Supervision

Maxime Oquab, Timothée Darcet, Théo Moutakanni et al., 2023. arXiv.

Introduces DINOv2, a family of vision models trained without labels to produce general-purpose image features.

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What it's used for here

The emerging-methods survey proposes it, with Segment Anything and CLIP, for unsupervised clustering and segmentation of glyphs.

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Cite it

Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., Assran, M., Ballas, N., Galuba, W., Howes, R., Huang, P. Y., Li, S. W., Misra, I., Rabbat, M., Sharma, V., Synnaeve, G., Xu, H., Jegou, H., Mairal, J., Labatut, P., Joulin, A., & Bojanowski, P. (2023). DINOv2: Learning Robust Visual Features without Supervision. arXiv. https://arxiv.org/abs/2304.07193
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