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 🤯

🛰️ 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

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)

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

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

💻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

🤔 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