1.1k citations · 1.2k across the 13 of their papers we have counts for
4 papers · 1 filter
Disentangling the Factors of Convergence between Brains and Computer Vision Models
Joséphine Raugel, Marc Szafraniec, Huy V. Vo +5
Many AI models trained on natural images develop representations that resemble those of the human brain. However, the factors that drive this brain-model similarity remain poorly u…
DINOv3
Oriane Siméoni, Huy V. Vo, Maximilian Seitzer +23
Self-supervised learning holds the promise of eliminating the need for manual data annotation, enabling models to scale effortlessly to massive datasets and larger architectures. B…
Back to the Features: DINO as a Foundation for Video World Models
Federico Baldassarre, Marc Szafraniec, Basile Terver +6
We present DINO-world, a powerful generalist video world model trained to predict future frames in the latent space of DINOv2. By leveraging a pre-trained image encoder and trainin…
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
Mido Assran, Adrien Bardes, David Fan +27
A major challenge for modern AI is to learn to understand the world and learn to act largely by observation. This paper explores a self-supervised approach that combines internet-s…