RESEARCH PAPERYear 2024

DINOv2: Learning Robust Visual Features without Supervision

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Maxime Oquab; Timothée Darcet; Théo Moutakanni; Huy V. Vo; Marc Szafraniec; Vasil Khalidov; Pierre Fernandez; Daniel Haziza; Francisco Massa; Alaaeldin El-Nouby; Mido Assran; Nicolas Ballas; Wojciech Galuba; Russell Howes; Po-Yao Huang; Shang-Wen Li; Ishan Misra; Michael Rabbat; Vasu Sharma; Gabriel Synnaeve; Hu Xu; Hervé Jégou; Julien Mairal; Patrick Labatut; Armand Joulin; Piotr Bojanowski

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Classification

View four quadrants

Major category: Components of WAMs
Quadrant: Not applicable
Architecture: Not applicable
Prediction paradigm: Not applicable
Subcategories: Visual encoders & representations
Source review status: Verified from primary sources

Category review. DINOv2 learns general self-supervised global and patch features, used by frozen-backbone probes and dense heads. Those transferable image embeddings directly satisfy the visual-representation component boundary. Reading evidence

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BibTeX

@article{dinov2,
  author       = {Maxime Oquab and
                  Timoth{\'{e}}e Darcet and
                  Th{\'{e}}o Moutakanni and
                  Huy V. Vo and
                  Marc Szafraniec and
                  Vasil Khalidov and
                  Pierre Fernandez and
                  Daniel Haziza and
                  Francisco Massa and
                  Alaaeldin El{-}Nouby and
                  Mido Assran and
                  Nicolas Ballas and
                  Wojciech Galuba and
                  Russell Howes and
                  Po{-}Yao Huang and
                  Shang{-}Wen Li and
                  Ishan Misra and
                  Michael Rabbat and
                  Vasu Sharma and
                  Gabriel Synnaeve and
                  Hu Xu and
                  Herv{\'{e}} J{\'{e}}gou and
                  Julien Mairal and
                  Patrick Labatut and
                  Armand Joulin and
                  Piotr Bojanowski},
  title        = {DINOv2: Learning Robust Visual Features without Supervision},
  journal      = {TMLR},
  year         = {2024},
  
}

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