31 citations · 31 across the 3 of their papers we have counts for
4 papers
Scalable Patch-Level Self-Supervised Learning
Maximilian Seitzer, Gabriele Trivigno, Antonín Vobecký +5
Self-supervised learning (SSL) at scale produces powerful visual representations. However, most scalable SSL methods rely on ad hoc combinations of multiple objectives and stabiliz…
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…
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…
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…