Computer Vision and Machine Learning at MPI Informatics
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cvml.mpi-inf.mpg.de
Computer Vision and Machine Learning at MPI Informatics
@cvml.mpi-inf.mpg.de
79 followers 39 following 50 posts
Computer Vision and Machine Department at the Max Planck Institute for Informatics | https://www.mpi-inf.mpg.de/departments/computer-vision-and-machine-learning/
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cvml.mpi-inf.mpg.de
AIM Project Page: eyad-alshami.github.io/aim-page/
AIM: Amending Inherent Interpretability via Self-Supervised Masking
AIM: Amending Inherent Interpretability via Self-Supervised Masking
eyad-alshami.github.io
October 21, 2025 at 10:37 AM
cvml.mpi-inf.mpg.de
cc: @janericlenssen.bsky.social al @xhxie.bsky.social
October 19, 2025 at 7:49 AM
cvml.mpi-inf.mpg.de
MVGBench: “A Comprehensive Benchmark for Multi-view Generation Models” — measures 3D consistency & image quality for fair comparisons.
By Xianghui Xie, Jan Eric Lenssen, Gerard Pons-Moll

Project: virtualhumans.mpi-inf.mpg.de/MVGBench/
October 19, 2025 at 7:49 AM
cvml.mpi-inf.mpg.de
cc: With @adagorgun.bsky.social
October 19, 2025 at 7:49 AM
cvml.mpi-inf.mpg.de
VITAL: “More Understandable Feature Visualization via Distribution Alignment & Relevant Information Flow.” Fewer artifacts, more faithful internals, scales well.

By Ada Görgün, Bernt Schiele, Jonas Fischer

Project: adagorgun.github.io/VITAL-Project/
October 19, 2025 at 7:49 AM
cvml.mpi-inf.mpg.de
cc: @eyadshami.bsky.social @shashank-agnihotri.bsky.social @margretkeuper.bsky.social
October 19, 2025 at 7:49 AM
cvml.mpi-inf.mpg.de
AIM (Highlight 🎉): “Amending Inherent Interpretability via Self-Supervised Masking.” - Promotes genuine features over spurious ones—no extra annotations.
By Eyad Alshami, Shashank Agnihotri, Bernt Schiele, Margret Keuper
October 19, 2025 at 7:49 AM
cvml.mpi-inf.mpg.de
cc: @oduenkel.bsky.social @wimmerthomas.bsky.social @adamkortylewski.bsky.social
October 19, 2025 at 7:49 AM
cvml.mpi-inf.mpg.de
DIY-SC: “Do It Yourself—Learning Semantic Correspondence from Pseudo-Labels.” - Light-weight adapter on DINOv2 / SD+DINOv2 → SOTA on SPair-71k w/o keypoints.
By O. Dünkel, T. Wimmer, C. Theobalt, C. Rupprecht, A. Kortylewski

Page: genintel.github.io/DIY-SC
October 19, 2025 at 7:49 AM
cvml.mpi-inf.mpg.de
📄 spatialreasoners.github.io
🔗 github.com/spatialreaso...
🌀Spatial Reasoners
spatialreasoners.github.io
July 13, 2025 at 8:00 AM
cvml.mpi-inf.mpg.de
4/ "🌀Spatial Reasoners for Continuous Variables in Any Domains" by @bartpog.bsky.social, @chriswewer.bsky.social, Bernt Schiele, and @janericlenssen.bsky.social (CODEML Workshop)

🔍 Software framework for training Spatial Reasoning Models in any domain
Image for "🌀Spatial Reasoners for Continuous Variables in Any Domains"
July 13, 2025 at 8:00 AM
cvml.mpi-inf.mpg.de
🔍 Can you really trust the explanations your classifier gives you? We show which pixels in the input are provably important to the classifier’s prediction within a radius around the input.

📄 openreview.net/pdf?id=NngoE...
🔗 github.com/AlaaAnani/ce...
openreview.net
July 13, 2025 at 8:00 AM
cvml.mpi-inf.mpg.de
3/ "Pixel-level Certified Explanations via Randomized Smoothing" by @aanani.bsky.social, Tobias Lorenz, Mario Fritz, and Bernt Schiele
Image for "Pixel-level Certified Explanations via Randomized Smoothing"
July 13, 2025 at 8:00 AM
cvml.mpi-inf.mpg.de
📄 arxiv.org/abs/2502.21075
🔗 geometric-rl.mpi-inf.mpg.de/srm/
🔗 github.com/Chrixtar/SRM
Spatial Reasoning with Denoising Models
We introduce Spatial Reasoning Models (SRMs), a framework to perform reasoning over sets of continuous variables via denoising generative models. SRMs infer continuous representations on a set of unob...
arxiv.org
July 13, 2025 at 8:00 AM