7 papers
Who Needs Labels? Adapting Vision Foundation Models With the Metadata You Already Have
Elouan Gardès, Seung Eun Yi, Kartik Ahuja +6
We propose a label-free approach to adapt powerful but generic vision foundation models to specialized scientific domains. Standard supervised fine-tuning is often ill-suited to th…
Misalignment Between Backpropagation and the Hierarchy of Brain Responses to Images
Joséphine Raugel, Maximilian Seitzer, Marc Szafraniec +6
Backpropagation is the core learning mechanism underlying deep learning. However, whether and how this algorithm is implemented in the brain remains highly debated. In particular,…
CHMv2: Improvements in Global Canopy Height Mapping using DINOv3
John Brandt, Seungeun Yi, Jamie Tolan +9
Accurate canopy height information is essential for quantifying forest carbon, monitoring restoration and degradation, and assessing habitat structure, yet high-fidelity measuremen…
Revisiting [CLS] and Patch Token Interaction in Vision Transformers
Alexis Marouani, Oriane Siméoni, Hervé Jégou +2
Vision Transformers have emerged as powerful, scalable and versatile representation learners. To capture both global and local features, a learnable [CLS] class token is typically…
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…