Imagine-ENPC

🏹 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 imagine-lab.enpc.fr/wp-content/upl…

We're hiring! IMAGINE @ École des Ponts (Paris area) is opening a 4-year "CV for X" researcher position: – competitive salary – no teaching load – starting pkg ≈ 2 PhDs – goal: impactful core AI + X (climate, biodiversity, robotics...) Apply by May 31: imagine-lab.enpc.fr/wp-content/upl…

🧩Excited to share our paper "RUBIK: A Structured Benchmark for Image Matching across Geometric Challenges" arxiv.org/abs/2502.19955 accepted to #CVPR2025! We created a benchmark that systematically evaluates image matching methods across well-defined geometric difficulty levels.

🧵 1/3 Many at #CVPR2024 & #ECCV2024 asked what would be next in our workshop series. We're excited to announce "How to Stand Out in the Crowd?" at #CVPR2025 Nashville - our 4th community-building workshop featuring this incredible speaker lineup! 🔗 sites.google.com/view/standoutcv…

Don't miss out on the premiere event bridging computer vision and Earth Observation! This year at #CVPR2025

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:

🌍 Guessing where an image was taken is a hard, and often ambiguous problem. Introducing diffusion-based geolocation—we predict global locations by refining random guesses into trajectories across the Earth's surface! 🗺️ Paper, code, and demo: nicolas-dufour.github.io/plonk

Airborne #LiDAR has revolutionized the study of ancient rainforest civilizations by seeing through dense canopies. Yet archaeologists still annotate their data manually. Introducing Archaeoscape at #NeurIPS2024—the first deep learning-scale, open-access archaeological dataset🧵👇