🛰️ Introducing UniverSat: one transformer backbone for Earth Observation that handles ANY sensor, ANY spatial, spectral & temporal resolution, ANY scale — with a single set of weights. 🌍

What if you could turn any number of photos (3, 8, 15, or even 60) into one clean 3D surface (pts & mesh) with Flow Matching? Check out our new work, Surflo: Consistent 3D Surface Flow Model with Global State. 🧵 1/N 🔗https://anttwo.github.io/surflo/

Excited to share my work as a Student Researcher at Google Zurich: UniGeoCLIP! 🌍🚀 W/ Eduard Trulls, Jan Hosang, @loicland.bsky.social & @pesarlin.bsky.social , we built a framework aligning 5 geospatial modalities in one space. Presented at EarthVision @ #CVPR2026. 🧵👇

🚨 arxiv.org/abs/2604.06129 PoM: A Linear-Time Replacement for Attention with the Polynomial Mixer This paper is the result of doing a lab-wide hackathon on an idea I've had for some time. Probably the paper with the highest number of authors I've ever done. It's a CVPR Findings 26. Thread 🧵👇

PoM: A Linear-Time Replacement for Attention with the Polynomial Mixer

This paper introduces the Polynomial Mixer (PoM), a novel token mixing mechanism with linear complexity that serves as a drop-in replacement for self-attention. PoM aggregates input tokens into a comp...

arxiv.org

We introduce MIRO: a new paradigm for T2I model alignment integrating reward conditioning into pretraining, eliminating the need for separate fine-tuning/RL stages. This single-stage approach offers unprecedented efficiency and control. - 19x faster convergence ⚡ - 370x less FLOPS than FLUX-dev 📉

Super interesting to see pure SSL outperforms text alignement on a super competitive but text-aligned suited task 🤯

Nicolas Dufour@nicolasdufour.bsky.social · 12mo ago

🚀 DinoV3 just became the new go-to backbone for geoloc! It outperforms CLIP-like models (SigLip2, finetuned StreetCLIP)… and that’s shocking 🤯 Why? CLIP models have an innate advantage — they literally learn place names + images. DinoV3 doesn’t.

🛰️ At #CVPR2025 presenting "AnySat: An Earth Observation Model for Any Resolutions, Scales, and Modalities" - Saturday afternoon, Poster 355! If you're here and want to discuss geolocation or geospatial foundation models, let's connect!

📢 FLAIR-HUB dataset A new large-scale, multimodal dataset for land cover and crop type mapping 🤗 Dataset: huggingface.co/datasets/IGN... 📄 Preprint: arxiv.org/abs/2506.07080 🤗 Pretrained models: huggingface.co/collections/... 💻 Code: github.com/IGNF/FLAIR-HUB 🌐 Project : arxiv.org/abs/2506.07080

FLAIR-HUB: Large-scale Multimodal Dataset for Land Cover and Crop Mapping

The growing availability of high-quality Earth Observation (EO) data enables accurate global land cover and crop type monitoring. However, the volume and heterogeneity of these datasets pose major pro...

arxiv.org

I will be presenting our work on the detection of archaeological looting with satellite image time series at CVPR 2025 EarthVision workshop tomorrow! Honored and grateful that this paper received the best student paper award!

📢 New preprint! “When majority rules, minority loses: bias amplification of gradient descent” We often blame biased data but training also amplifies biases. Our paper explores how ML algorithms favor stereotypes at the expense of minority groups. ➡️ arxiv.org/abs/2505.13122 (1/3)

When majority rules, minority loses: bias amplification of gradient descent

Despite growing empirical evidence of bias amplification in machine learning, its theoretical foundations remain poorly understood. We develop a formal framework for majority-minority learning tasks, ...

arxiv.org

We've added new experiments demonstrating robust generalization capabilities! Notably, AnySat shows strong performance on HLS Burn Scars - a sensor never seen during pretraining! 🔥🛰️ Check it out: 📄 Paper: arxiv.org/abs/2412.14123 🌐 Project: gastruc.github.io/anysat

Loïc Landrieu @loicland.bsky.social · last yr.

AnySat has been accepted as a ✨ highlight at #CVPR2025! See you in Nashville 🎉 We’ll also be presenting this work at: 📍 @egu.eu on 02/04 in Vienna 📍 @esa.int / NASA Workshop on Foundation Models on 05/04 in Rome

Looking forward to #CVPR2025! We will present the following papers:

Introducing HySCDG #CVPR2025, a generative pipeline for creating a large hybrid semantic change detection for Earth Observation using Stable Diffusion and ControlNet ! 🗺️🛩️ 📄 Paper: arxiv.org/abs/2503.15683

The Change You Want To Detect: Semantic Change Detection In Earth Observation With Hybrid Data Generation

Bi-temporal change detection at scale based on Very High Resolution (VHR) images is crucial for Earth monitoring. This remains poorly addressed so far: methods either require large volumes of annotate...

arxiv.org

💻We've released the code for our #CVPR2025 paper MAtCha! 🍵MAtCha reconstructs sharp, accurate and scalable meshes of both foreground AND background from just a few unposed images (eg 3 to 10 images)... ...While also working with dense-view datasets (hundreds of images)!

🔥🔥🔥 CV Folks, I have some news! We're organizing a 1-day meeting in center Paris on June 6th before CVPR called CVPR@Paris (similar as NeurIPS@Paris) 🥐🍾🥖🍷 Registration is open (it's free) with priority given to authors of accepted papers: cvprinparis.github.io/CVPR2025InPa... Big 🧵👇 with details!

Starter pack including some of the lab members: go.bsky.app/QK8j87w

🧩 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. 🔍

Weights for CAD are finally available. It's one of the smallest diffusion models on the market, achieving performance close to SD and Pixart, featuring a Perceiver-like architecture. We leverage our coherence aware training to improve the textual understanding

David Picard@davidpicard.eurosky.social · last yr.

🚨 Just a quick note that following requests, we trained a 512px version of our Coherence-Aware Diffusion model (CVPR'24) and updated the paper on arxiv: arxiv.org/abs/2405.20324 It has a package and pretrained models! 🖥️ nicolas-dufour.github.io/cad.html 🤖 github.com/nicolas-dufo...

🤔 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:

Guillaume Astruc, Nicolas Gonthier, Clement Mallet, Loic Landrieu AnySat: An Earth Observation Model for Any Resolutions, Scales, and Modalities https://arxiv.org/abs/2412.14123

⚠️Reconstructing sharp 3D meshes from a few unposed images is a hard and ambiguous problem. ☑️With MAtCha, we leverage a pretrained depth model to recover sharp meshes from sparse views including both foreground and background, within mins!🧵 🌐Webpage: anttwo.github.io/matcha/

🌍 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