Had so much fun at #CVPR2026! 🎉 Really happy to have presented two papers this year and discussed them with so many people. Both projects came out of my time at @visinf, TU Darmstadt, last year. Very grateful to @stefanroth for hosting me and helping shape them from the beginning, and to all my co-authors for being part of this 😊

INSID3 segments objects across domains using ONLY ONE annotated example it works entirely without a segmentation decoder, task-specific fine-tuning, or external mask generators like SAM CVPR 2026 paper with enormous practical potential

Today! I'm going to present MARCO in Oral Session 4D at 2 pm, and later at poster #20! Don't miss it :)

✨ #CVPR2026 Oral ✨ INSID3 turns a frozen DINOv3 into a training-free in-context segmenter across domains and granularities! Excited to present our work today in Oral Session 4D (14:00–15:15). Come by our poster later if you’d like to chat: 📍 Poster #19 🕓 16:00–18:00 See you there!

In-context learning suggests that a model has learned versatile representations. What if we use in-context learning itself as a training task for visual representations? 📣 Introducing 𝗟𝗜𝗟𝗔: 𝗟𝗶𝗻𝗲𝗮𝗿 𝗜𝗻-𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 ✨ @CVPR 2026 Oral ✨ 𝗟𝗜𝗟𝗔 trains on videos without manual annotation. Key idea: An optimal linear mapping that predicts dense cues (e.g. depth, flow), estimated on one video frame, should also predict the corresponding cues of another frame from the same video. This yields compelling results on dense vision tasks: video object segmentation, (zero-shot) semantic segmentation and surface normal estimation. Paper, code, models and demo: lila-pixels.github.io Joint work with @ma_sundermeyer, Hidenobu Matsuki, David Joseph Tan and @fedassa (and special thanks to David and Federico for hosting my research visit at Google). #cvpr2026 @Google @MunichCenterML @tumcvg @TU_Muenchen