Excited to share that MIRO is accepted to ICML 2026 @icmlconf ! 🎉
We introduce multi-reward conditioned training for text-to-image. By training on continuous reward scores, we can simply condition on HIGH REWARDS at inference to guarantee top-tier, aligned outputs.
Excited to share my work as a Student Researcher at Google Zurich: UniGeoCLIP! 🌍🚀
W/ Eduard Trulls, Jan Hosang, @loiclandrieu & @pesarlin, we built a framework aligning 5 geospatial modalities in one space.
Presented at EarthVision @ #CVPR2026. 🧵👇
🏹 Job alert: 4-year position as Research Fellow in Computer Vision for X at @ImagineEnpc
Goal: impactful core AI + X (climate, biodiversity, robotics...)
📍 Paris
➡️ Apply by May 31
https://t.co/0ABhGRuG4A
🧩Excited to share our paper "RUBIK: A Structured Benchmark for Image Matching across Geometric Challenges" https://t.co/7vbufXdITE accepted to #CVPR2025! We created a benchmark that systematically evaluates image matching methods across well-defined geometric difficulty levels.
Very happy to be at @c2rmf with Mathieu Aubry @ImagineEnpc to present our platforms dedicated to AI applied to historical and cultural documents #AIKON#Arkindex@_Teklia_
🤔 What if embedding multimodal EO data was as easy as using a ResNet on images?
Introducing AnySat: one model for any resolution (0.2m–250m), scale (0.3–2600 hectares), and modalities (choose from 11 sensors & time series)!
Try it with just a few lines of code: