3 papers
cs.CV2026
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…
cs.CV2026
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…
cs.CV2025
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…