LinkedIn
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Dive into the details and code:
📖 lnkd.in/d4FjwFY7 (paper)
🖨️ lnkd.in/dkdabrZ9 (pre-print)
💾 lnkd.in/dfnUk8-C (code)
#FAU #DFG #MachineLearning #ComputerVision
29.03.2025 07:51 — 👍 0 🔁 0 💬 0 📌 0
“Revisiting Gradient-Based Uncertainty for Monocular Depth Estimation” IEEE TPAMI, is here! We present a further formulation of our gradient-based method for quantifying #uncertainty in monocular #depth predictions #NoTrainingNeeded.
Joint work between @fau.de & @uniulm.bsky.social.
Links below.
29.03.2025 07:51 — 👍 0 🔁 0 💬 1 📌 0
Excited to share that today our paper recommender platform www.scholar-inbox.com has reached 20k users! We hope to reach 100k by the end of the year.. Lots of new features are being worked on currently and rolled out soon.
15.01.2025 22:03 — 👍 190 🔁 26 💬 12 📌 8
Interesting, it appears the #ICCV2025 submission and supplementary materials deadlines are the SAME.
05.01.2025 00:46 — 👍 18 🔁 3 💬 3 📌 0
Visual results
25.12.2024 04:42 — 👍 0 🔁 0 💬 0 📌 0
LinkedIn
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Results: Extensive evaluation on ImageNet and CIFAR-10 datasets shows superior performance in filtering low-quality samples and improving generation quality.
📄 Paper: lnkd.in/d7JBSkiz
💻 Code: lnkd.in/dqFbC2nP
Online demo coming soon!
#FAU #MachineLearning #ComputerVision #DiffusionModels #WACV2025
24.12.2024 05:39 — 👍 0 🔁 0 💬 1 📌 0
💡 Theoretical Insights: We show that our uncertainty estimates are related to the second-order derivative of the diffusion noise distribution, providing a solid mathematical foundation.
24.12.2024 05:38 — 👍 0 🔁 0 💬 1 📌 0
🎯 Guided sampling: We use uncertainty estimates to drive the sampling process towards higher quality generations, resulting in improved FID results and fewer artefacts.
24.12.2024 05:38 — 👍 0 🔁 0 💬 1 📌 0
Key highlights:
📊 Training-free uncertainty: Our method estimates pixel-wise aleatoric uncertainty during the sampling phase without requiring any model modifications or additional training, allowing to filter out low quality samples.
24.12.2024 05:38 — 👍 0 🔁 0 💬 1 📌 0
🎉 🎄 New WACV 2025 Publication!
"Diffusion Model Guided Sampling with Pixel-Wise Aleatoric Uncertainty Estimation" by Michele De Vita, Vasileios Belagiannis @fau.de
We're excited to introduce one of the first uncertainty estimation methods for diffusion models!
Links & highlights below
24.12.2024 05:38 — 👍 1 🔁 0 💬 1 📌 0
Computer Vision PhD at MIT
georgecazenavette.github.io/
Assistant Professor in Computer Science at UofT.
PhD Candidate in 3D CV @CogCoVi.bsky.social @FAU.de
Former Intern at RealityLabs, SamsungResearch
andreeadogaru.github.io
Student Researcher @ RAI Institute, MSc CS Student @ ETH Zurich
visual computing, 3D vision, spatial AI, machine learning, robot perception.
📍Zurich, Switzerland
Official Account for the European Conference on Computer Vision (ECCV) #ECCV2026, Malmo 🇸🇪 Hosted by @jbhaurum and @CSProfKGD
Official account for the IEEE/CVF International Conference on Computer Vision. #ICCV2025 Honolulu 🇺🇸 Co-hosted by @natanielruiz @antoninofurnari @yaelvinker @CSProfKGD
Ph.D. Student @ VRG, CTU in Prague, CZ. Previously ECE @ NTUA, GR.
Mainly focused on fine-grained image representations that scale.
Professor for CS at the Tuebingen AI Center and affiliated Professor at MIT-IBM Watson AI lab - Multimodal learning and video understanding - GC for ICCV 2025 - https://hildekuehne.github.io/
Professor in applied mathematics at University of Orléans 🇫🇷 & IUF Junior 2022 on an innovation chair.
Interested in image processing and deep generative models.
I teach generative models for imaging at #MasterMVA.
Principal Research Scientist in Computer Vision at Naver Labs Europe
https://philippeweinzaepfel.github.io/
Research scientist 🍇🖥️🌱🦠🔬
Plant Pathology | 3D+t imaging | MRI | Xrays tomography | IA | Image Analysis
Trunk diseases | Fungal Pathogens | Genetics | Molecular Biology | Grapevine | Breeding & Selection
Working at French Wine and Vine Institute
France
PhD student at MIT. Machine learning, computer vision, ecology, climate. Previously: Co-founder, CTO Ai.Fish; Researcher at Caltech; UC Berkeley. justinkay.github.io
PhD Student at the Max Planck Institute for Informatics @cvml.mpi-inf.mpg.de @maxplanck.de | Explainable AI, Computer Vision, Neuroexplicit Models
Web: sukrutrao.github.io
PhD student @ TUM with Daniel Cremers
Senior scientist at valeo.ai
Senior Researcher at Max Planck Institute for Informatics
Founding Engineer at Kumo.ai
PhD Student in Computer Vision w/ @stefanroth.bsky.social at @visinf.bsky.social
More: olvrhhn.github.io
Post-Doctoral Researcher at @eml-munich.bsky.social, in
@www.helmholtz-munich.de and @tumuenchen.bsky.social.
Optimal Transport, Explainability, Robustness, Deep Representation Learning, Computer Vision.
https://qbouniot.github.io/
