Thrilled to share that our paper, "Where's Waldo: Diffusion Features for Personalized Segmentation and Retrieval" has been accepted to NeurIPS 2024! 🎉 Paper: arxiv.org/abs/2405.18025 Project Page: dvirsamuel.github.io/pdm.github.io/ #NeurIPS2024 Gal Chechik Rami Ben-Ari Matan Levy

Super interesting work by my ex-officemate @NivCohenHuji

[1/10] 🚨 We present our recent Snap Inc. project: Stable Flow --- A training-free method that performs various types of image editing operations (e.g., non-rigid editing, object addition and replacement) using flow models. Project page: omriavrahami.com/stable-flow

It was a pleasure to co-organize this workshop with the amazing Avital Shafran! Can’t wait to see what she accomplishes next! 👏

Release Announcement!📢💣 🥁🎷JASCO 🎶🪇 training & inference code + model weights are out! Paper📜: arxiv.org/abs/2406.10970 Samples🔊: pages.cs.huji.ac.il/adiyoss-lab/JA… Code🐍: github.com/facebookresear… Models🤗: huggingface.co/facebook/jasco… Alon Ziv Itai Gat Felix Kreuk Yossi Adi

Who recognizes where it is? 🔥 Excited to share that our paper, led by Issar Tzachor, "EffoVPR: Effective Foundation Model Utilization for Visual Place Recognition," has been accepted at #ICLR2025! The VPR task aims to answer this question ⬇️

🗣️🧠 Speech Language Models require lots of compute to train, right? In our new paper, we test is it possible to train an SLM on 1xA5000 gpu in 24 hours? The results may surprise you (they even surprised us)! Tips, open source resources, full paper 👇🏻

Care about LLM evaluation? 🤖 🤔 We bring you🕊️ DOVE a massive (250M!) collection of LLMs outputs On different prompts, domains, tokens, models... Join our community effort to expand it with YOUR model predictions & become a co-author!

In our #ICLR2025 paper, we introduce WIND 🌬️ A method that embeds a distortion watermark directly in the diffusion noise! Our method ensures that the watermark in one image does not reveal information about the watermark in other images 🤫 📝 arxiv.org/abs/2412.04653 (1/5)

As I said before - everything the (very!) soon to be Dr. Avrahami does is incredible

Think your latent-noise diffusion watermarking method is robust? Think again! We show that they are susceptible to adversarial attacks that only require one watermarked example and an off-the-shelf encoder. This attack can forge and remove the watermark with very high accuracy

When we "erase" a concept from a diffusion model, is that knowledge truly gone? 🤔 We investigated, and the answer is often 'no'! Using simple probing techniques, the knowledge traces of the erased concept can be easily resurfaced 🔍 Here is what we learned 🧵👇