🔥🔥🔥 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!
By unifying detection and reconstruction, our approach predicts accurate bounding boxes to deforms these learned prototypes. This unlocks objective metrics (like aspect ratios, bi-gram distances) without any manual segmentation! 📐
w/ M. Vlachou , /D. Stuzmann /M. Aubry
I wrote a notebook for a lecture/exercice on image generation with flow matching. The idea is to use FM to render images composed of simple shapes using their attributes (type, size, color, etc). Not super useful but fun and easy to train!
colab.research.google.com/drive/16GJyb...
Comments welcome!
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
Very excited to share our new paper, accepted at ICDAR 2026: "Leveraging Morphology for Historical Script Metrological Analysis"! 📜We present a method to automatically extract high-quality character prototypes and compute stable paleographic measurements:
malamatenia.github.io/morphology4m...
1/n🚀Gaussians > Differentiable function > Mesh?
Check out our new work: MILo: Mesh-In-the-Loop Gaussian Splatting!
🎉Accepted to SIGGRAPH Asia 2025 (TOG)
MILo is a novel differentiable framework that extracts meshes directly from Gaussian parameters during training.
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Starter pack including some of the lab members: go.bsky.app/QK8j87w
🍏 New preprint alert! 🍏
PoM: Efficient Image and Video Generation with the Polynomial Mixer
arxiv.org/abs/2411.12663
This is my latest "summer project" and it was so big I had to call in reinforcements (Thanks @nicolasdufour.bsky.social)
TL;DR Transformers are for boomers, welcome to the future
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I'm slowly putting my intro to ML course material on github, starting with the lab sessions: github.com/davidpicard/...
These are self-contained notebooks in which you have to implement famous algorithms from the literature (k-NN, SVM, DT, etc), with a custom dataset that I (painstakingly) made!