UniverSat: Resolution- and Modality-Agnostic Transformers for Earth Observation
UniverSat introduces a Universal Patch Encoder for Vision Transformers that enables robust, sensor-agnostic spatial feature extraction across diverse Earth Observation data types.
Hugging Face · Daily Papers
·Yohann Perron, Guillaume Astruc
·
·▲ 2 upvotes
Este artigo está em destaque na seleção diária de papers do Hugging Face, curada pela comunidade de pesquisa em IA.
Autores: Yohann Perron, Guillaume Astruc, Nicolas Gonthier, Clement Mallet, Loic Landrieu
- 2 upvotes da comunidade
- Temas: Vision Transformers, Universal Patch Encoder, patch projectors, Earth Observation, multimodal corpora, self-supervision
Resumo
Resumo original (em inglês), extraído do paper:
UniverSat introduces a Universal Patch Encoder for Vision Transformers that enables robust, sensor-agnostic spatial feature extraction across diverse Earth Observation data types.
// relacionados
Leia também
Blog
Do Medical Foundation Models Generalize on the African Brain?
Blog
A Unified Benchmark of Deep Learning Models for Multi-task 3D Brain Tumor Segmentation from Magnetic Resonance Imaging
Blog
RayViT: Ray-Conditioned Visual Representations for Viewpoint-Robust Imitation Learning
Blog