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ObjectDrop is accepted to #ECCV2024! 🥳 In this work from Google AI we tackle photorealistic object removal and insertion. Congrats to the team: Matan Cohen Shlomi Fruchter Yael Pritch  Alex Rav-Acha Yedid Hoshen Checkout our project page: objectdrop.github.io

ObjectDrop accepted to ECCV! 🥳🎉

With friends at Google we announce 💜 Magic Insert 💜 - a generative AI method that allows you to drag-and-drop a subject into an image with a vastly different style achieving a style-harmonized and realistic insertion of the subject (Thread 🧵) web: magicinsert.github.io

Another chance to see our poster for those that missed ICML

Amazing work by my colleagues!

Check out our latest works on model weight learning!

Check out our latest work!

🚀 ICLR 2026 notification, 🚀 #CVPR2025 rebuttal, 🚀 ICML Conference submission - nothing is as fun as submitting a paper to our 🔥 Weight Space Learning Workshop 🔥 at ICLR 2026 Interested? Have a look at: weight-space-learning.github.io Konstantin Schürholt,Giorgos Bouritsas (@[email protected]), Eliahu Horwitz,

Organized by: Konstantin Schürholt Giorgos Bouritsas (@[email protected]) Eliahu Horwitz Derek Lim Yoav Gelberg Bo Zhao Allan Zhou Stefanie Jegelka Michael Bronstein Gal Chechik Stella Yu Haggai Maron Yedid Hoshen Join us in shaping this emerging field! 🚀

⏳Final hours to submit to the 1st workshop on Weight Space Learning at #ICLR2025 @ICLR_conf❗ 🚀Explore learning directly from model weights! 🌐 weight-space-learning.github.io ⏰ Submit by Feb 10 2025 AoE

With so many models one is left asking, how do find the relevant model for me?🔍🕵‍♂ Here's our stab at tackling this question👀 First attempt: horwitz.ai/probex And fresh off the press is a very exciting direction led by Jonathan Kahana jonkahana.github.io/probelog/

🚨 New Paper Alert on Zero-Shot Model Search 🚨 We present an efficient an accurate method to search and understand what models can do, based on their weights without access to any metadata! Project Page 🌐: jonkahana.github.io/probelog/

Daniel van Strien Hugging Face This is awesome! But what about the 700k+ models with no model cards? Here's our stab at tackling this problem for models with no metadata 👀 Our #CVPR2025 paper horwitz.ai/probex And a fresh and exciting direction led by Jonathan Kahana jonkahana.github.io/probelog/

this paper is a visual art! the atlas of🤗hugging face models studies 63,000 models published on Hugging Face and they share some fun numbers: > 96% of vision models are ≤1 node from foundation models, but 5% of NLP models reach ≥5 node depths > only 0.15% of vision models

Charting and Navigating Hugging Face's Model Atlas

🚨 Update Alert! 🚨 • ⚡ Much faster loading (seconds, not minutes!) • ✨ Fresh layout for Large NLP Atlas • 📖 Clear explanations of all features Stay tuned—more improvements coming soon!

What if models could be the data🤔Find out at ICLR 2026 #ICLR2025 Join the 1st workshop on Model Weights as a New Data Modality. We're training networks on model weights for a wide variety of tasks. Featuring an amazing lineup of papers & speakers🚀 🗓️Sunday 9-17 📍Topaz 220-225

Check out our latest work!

Andrej Karpathy Thanks for the inspiring talk (as always!). I'm the author of the Model Atlas. I'm delighted you liked our work, seeing the figure in your slides felt like an "achievement unlocked"🙌Would really appreciate a link to our work in your slides/tweet arxiv.org/abs/2503.10633