Wonderful story behind some very nice SSL work!

I'm in Nashville this week attending #CVPR2025. Excited to discuss post-training VLMs and diffusion models!

We're super happy: Our Cluster of Excellence will continue to receive funding from the German Research Foundation @dfg.de ! Here’s to 7 more years of exciting research at the intersection of #machinelearning and science! Find out more: uni-tuebingen.de/en/research/... #ExcellenceStrategy

🚨 New preprint! How far can we go with ImageNet for Text-to-Image generation? w. @arrijitghosh.bsky.social @lucasdegeorge.bsky.social @nicolasdufour.bsky.social @vickykalogeiton.bsky.social TL;DR: Train a text-to-image model using 1000 less data in 200 GPU hrs! 📜https://arxiv.org/abs/2502.21318 🧵👇

These are some ridiculously good results from training tiny T2I models purely on ImageNet! It's almost too good to be true. Do check it out!

I've been talking about writing this paper to anyone who would listen since 2020. I bombed a bunch of job talks trying to convince companies to work on this. It's so nice to finally just be able to say, yes, self-play RL in a diverse world gives you immense capabilities arxiv.org/abs/2502.03349

🚨Great Models Think Alike and this Undermines AI Oversight🚨 New paper quantifies LM similarity (1) LLM-as-a-judge favor more similar models🤥 (2) Complementary knowledge benefits Weak-to-Strong Generalization☯️ (3) More capable models have more correlated failures 📈🙀 🧵👇

ReNO shows that some initial noise are better for some prompts! This is great to improve image generation, but i think it also shows a deeper property of diffusion models.

This is maybe my favorite thing I've seen out of #NeurIPS2024. Head over to HuggingFace and play with this thing. It's quite extraordinary.

Can we enhance the performance of T2I models without any fine-tuning? We show that with our ReNO, Reward-based Noise Optimization, one-step models consistently surpass the performance of all current open-source Text-to-Image models within the computational budget of 20-50 sec! #NeurIPS2024

I will present ✌️ BDU workshop papers @ NeurIPS: one by Rui Li (looking for internships) and one by Anton Baumann. 🔗 to extended versions: 1. 🙋 "How can we make predictions in BDL efficiently?" 👉 arxiv.org/abs/2411.18425 2. 🙋 "How can we do prob. active learning in VLMs" 👉 arxiv.org/abs/2412.06014

After a break of over 2 years, I'm attending a conference again! Excited to attend NeurIPS, even more so to be presenting ReNO, getting inference-time scaling and preference optimization to work for text-to-image generation. Do reach out if you'd like to chat!

🚨New Paper Alert🚨 🚀 Introducing FlowChef, "Steering Rectified Flow Models in the Vector Field for Controlled Image Generation"! 🌌✨ - Perform image editing, solve inverse problems, and more. - Achieved inversion-free, gradient-free, & training-free inference time steering! 🤯 👇👇

Some recent discussions made me write up a short read on how I think about doing computer vision research when there's clear potential for abuse. Alternative title: why I decided to stop working on tracking. Curious about other's thoughts on this. lb.eyer.be/s/cv-ethics....

Check out this nice work by @confusezius.bsky.social on designing VLMs for few-shot adaptation!

A real-time (or very fast) open-source txt2video model dropped: LTXV. HF: huggingface.co/Lightricks/L... Gradio: huggingface.co/spaces/Light... Github: github.com/Lightricks/L... Look at that prompt example though. Need to be a proper writer to get that quality.

Learning from one continuous video stream - use a video stream to learn a predictive model - everything is in pixel space - update the model less frequently and don’t use momentum optimizer - pre training with iid improves performance - continual learning for robots arxiv.org/html/2312.00...

Here's a fledgling starter pack for the AI community in Tübingen. Let me know if you'd like to be added! go.bsky.app/NFbVzrA