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Timo Lüddecke

@timojl.bsky.social
8 followers 33 following 8 posts

University of Göttingen and CIDAS

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Timo Lüddecke @timojl.bsky.social · 02/06/2026
More Information: 📝 Paper: openaccess.thecvf.com/content/CVPR... 🎬 Video: www.youtube.com/watch?v=qZtY... 💻 Code: github.com/timojl/lidere
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Timo Lüddecke @timojl.bsky.social · 02/06/2026
🚀 Introducing LiDeRe: a lightweight readout that turns frozen vision backbones (e.g. DINOv3) into strong dense predictors. A tiny head → semantic segmentation, detection, pose, contour. Often beats SoTA methods with a fraction of the trainable parameters. At #CVPR2026, poster session 1 on Friday.
LiDeRe summary figure
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Timo Lüddecke @timojl.bsky.social · 28/11/2025
This is joint work with @aecker.bsky.social
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Timo Lüddecke @timojl.bsky.social · 28/11/2025
More information (with additional results on DinoV3, SigLIP2 and Perception Encoder): 📄 Paper (in TMLR): openreview.net/forum?id=neM... 📊 Website: eckerlab.org/projects/deap/ 💻 Code: github.com/timojl/deap …or drop by our poster at the ELLIS UnConference on December 2nd in Copenhagen. #EuRIPS
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Timo Lüddecke @timojl.bsky.social · 28/11/2025
Based our performance data for all backbones, we analyze to which degree performance can be attributed to general properties of the backbone (input image resolution, feature dimension, number of parameters). We find strong relationships with all properties for semantic segmentation and depth.
plot on relationship between performance and backbone properties
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Timo Lüddecke @timojl.bsky.social · 28/11/2025
A closer look into the three instance awareness tasks (instance discrimination, instance boundary detection, object detection) reveals that self-supervised learning outperforms vision-language (CLIP-style) pretraining.
relative performance-runtime plot
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Timo Lüddecke @timojl.bsky.social · 28/11/2025
We compare supervised, self-supervised and vision-language backbones with respect to instance awareness, local semantics and spatial understanding. Here we show the trade-off between forward pass runtime and local semantics and spatial understanding performance:
performance-runtime plots
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Timo Lüddecke @timojl.bsky.social · 28/11/2025
📣 Paper alert: We present dense attentive probing (DeAP), a method to measure the representation quality of various vision backbones for dense prediction tasks. It uses small, parameter-efficient readouts with learnable masks to generate dense predictions from backbone features of any size.
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