Some impressions from our VISINF summer retreat at Lizumer Hütte in the Tirol Alps — including a hike up Geier Mountain and new research ideas at 2,857 m! 🇦🇹🏔️

Check out our blog post about SceneDINO 🦖 For more details, check out our project page, 🤗 demo, and the hashtag #ICCV2025 paper 🚀 🌍Project page: visinf.github.io/scenedino/ 🤗Demo: visinf.github.io/scenedino/ 📄Paper: arxiv.org/abs/2507.06230 @jev-aleks.bsky.social

𝗙𝗲𝗲𝗱-𝗙𝗼𝗿𝘄𝗮𝗿𝗱 𝗦𝗰𝗲𝗻𝗲𝗗𝗜𝗡𝗢 𝗳𝗼𝗿 𝗨𝗻𝘀𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗦𝗰𝗲𝗻𝗲 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 Aleksandar Jevtić, Christoph Reich, Felix Wimbauer ... Daniel Cremers arxiv.org/abs/2507.06230 Trending on www.scholar-inbox.com

The code for our #CVPR2025 paper, PRaDA: Projective Radial Distortion Averaging, is now out! Turns out distortion calibration from multiview 2D correspondences can be fully decoupled from 3D reconstruction, greatly simplifying the problem arxiv.org/abs/2504.16499 github.com/DaniilSinits...

🦖 We present “Feed-Forward SceneDINO for Unsupervised Semantic Scene Completion”. #ICCV2025 🌍: visinf.github.io/scenedino/ 📃: arxiv.org/abs/2507.06230 🤗: huggingface.co/spaces/jev-a... @jev-aleks.bsky.social @fwimbauer.bsky.social @olvrhhn.bsky.social @stefanroth.bsky.social @dcremers.bsky.social

Aleksandar Jevti\'c, Christoph Reich, Felix Wimbauer, Oliver Hahn, Christian Rupprecht, Stefan Roth, Daniel Cremers Feed-Forward SceneDINO for Unsupervised Semantic Scene Completion https://arxiv.org/abs/2507.06230

Got a strong XAI paper rejected from ICCV? Submit it to our ICCV eXCV Workshop today—we welcome high-quality work! 🗓️ Submissions open until June 26 AoE. 📄 Got accepted to ICCV? Congrats! Consider our non-proceedings track. #ICCV2025 @iccv.bsky.social

We had a great time at #CVPR2025 in Nashville!

Scene-Centric Unsupervised Panoptic Segmentation by @olvrhhn.bsky.social , @christophreich.bsky.social , @neekans.bsky.social , @dcremers.bsky.social, Christian Rupprecht, and @stefanroth.bsky.social Sunday, 8:30 AM, ExHall D, Poster 330 Project Page: visinf.github.io/cups

Can we match vision and language representations without any supervision or paired data? Surprisingly, yes!  Our #CVPR2025 paper with @neekans.bsky.social and @dcremers.bsky.social shows that the pairwise distances in both modalities are often enough to find correspondences. ⬇️ 1/4

Can you train a model for pose estimation directly on casual videos without supervision? Turns out you can! In our #CVPR2025 paper AnyCam, we directly train on YouTube videos and achieve SOTA results by using an uncertainty-based flow loss and monocular priors! ⬇️

Check out our latest recent #CVPR2025 paper AnyCam, a fast method for pose estimation in casual videos! 1️⃣ Can be directly trained on casual videos without the need for 3D annotation. 2️⃣ Based around a feed-forward transformer and light-weight refinement. Code and more info: ⏩ fwmb.github.io/anycam/

Check out our recent #CVPR2025 #highlight paper on unsupervised panoptic segmentation🚀 🌍 visinf.github.io/cups/

Check out the #MCML blog post on our recent #CVPR2025 #highlight paper🔥

Check out the recent CVG papers at #CVPR2025, including our (@olvrhhn.bsky.social, @neekans.bsky.social, @dcremers.bsky.social, Christian Rupprecht, and @stefanroth.bsky.social) work on unsupervised panoptic segmentation. The paper will soon be available on arXiv. 🚀

🏔️⛷️ Looking back on a fantastic week full of talks, research discussions, and skiing in the Austrian mountains!

This week we had our winter retreat jointly with Daniel Cremer's group in Montafon, Austria. 46 talks, 100 Km of slopes and night sledding with some occasionally lost and found. It has been fun!

Want to learn about how model design choices affect the attribution quality of vision models? Visit our #NeurIPS2024 poster on Friday afternoon (East Exhibition Hall A-C #2910)! Paper: arxiv.org/abs/2407.11910 Code: github.com/visinf/idsds

Our work, "Boosting Unsupervised Semantic Segmentation with Principal Mask Proposals" is accepted at TMLR! 🎉 visinf.github.io/primaps/ PriMaPs generate masks from self-supervised features, enabling to boost unsupervised semantic segmentation via stochastic EM.