Excited to share that our paper, “Active Learning via Classifier Impact and Greedy Selection for Interactive Image Retrieval”, has been accepted to TMLR! TMLR: openreview.net/pdf?id=b68QOen… Project Page: github.com/barleah/Greedy… Short Video Presentation: youtu.be/bHDARDpu8Fg

🎉 I'm happy to share that our paper, Make It Count, has been accepted to #CVPR2025! A huge thanks to my amazing collaborators - Yoad Tewel, Hilit Segev , Eran Hirsch, Royi Rassin, and Gal Chechik! 🔗 Paper page: make-it-count-paper.github.io Excited to share our key findings!

🚀Introducing SISO – a plug-and-play approach for image personalization using just one image!

🎉 Thank you AK for featuring our work! We’re happy to release the 💻 code & 🤗 Hugging Face demo for Add-it, our #ICLR2025 paper: 💻 Code: github.com/NVlabs/addit 🤗 Demo: huggingface.co/spaces/nvidia/…

Can AI coscientists help automate ablation planning? To test this, we created AblationBench, a benchmark suite to evaluate models on ablation planning in empirical AI research. The results? Even the best current models recover only 29% of the original ablations on average ⬇️

What if video editing took seconds instead of hours? OmnimatteZero – by Dvir Samuel Prof. Gal Chechik from אוניברסיטת בר-אילן, HUJI & OriginAI – Removes dynamic objects with their shadows and reflections, separates layers, and reinserts them in real time. 👉 arxiv.org/abs/2503.18033

🎉 I am excited to present our new paper! Our paper improves personalization of text-to-image models, by adding one special cleaning step on top of existing personalized models. With just a single gradient update (~4 seconds on an NVIDIA H100 GPU) and a single image of the