2 citations · 3 across the 8 of their papers we have counts for
10 papers · 1 filter
Two Global Crops Suffice: Locating Semantic Emergence in DINO-Style Self-Supervised Learning
Basavaraj Sunagad, Artur Jesslen, Adam Kortylewski
Self-supervised vision transformers trained with DINO-style objectives exhibit striking emergent semantic representation quality across visual tasks, yet the mechanisms underlying…
Geometry Matters: 3D Foundation Priors for Learning Semantic Correspondence
Artur Jesslen, Olaf Dünkel, Adam Kortylewski
Foundation features from self-supervised vision models and text-to-image diffusion models have proven effective for semantic correspondence estimation. However, because these featu…
Category-Level 3D Correspondence in Camera Space via Morphable Object Priors
Leonhard Sommer, Artur Jesslen, Basavaraj Sunagad +1
Understanding 3D objects from images is fundamental to robotics and AR/VR applications. While recent work has made progress in category-level pose estimation, current representatio…
CNS-Bench: Benchmarking Image Classifier Robustness Under Continuous Nuisance Shifts
Olaf Dünkel, Artur Jesslen, Jiahao Xie +3
An important challenge when using computer vision models in the real world is to evaluate their performance in potential out-of-distribution (OOD) scenarios. While simple synthetic…
Interpretable 3D Neural Object Volumes for Robust Conceptual Reasoning
Nhi Pham, Artur Jesslen, Bernt Schiele +2
With the rise of deep neural networks, especially in safety-critical applications, robustness and interpretability are crucial to ensure their trustworthiness. Recent advances in 3…
iNeMo: Incremental Neural Mesh Models for Robust Class-Incremental Learning
Tom Fischer, Yaoyao Liu, Artur Jesslen +6
Different from human nature, it is still common practice today for vision tasks to train deep learning models only initially and on fixed datasets. A variety of approaches have rec…
Unsupervised Learning of Category-Level 3D Pose from Object-Centric Videos
Leonhard Sommer, Artur Jesslen, Eddy Ilg +1
Category-level 3D pose estimation is a fundamentally important problem in computer vision and robotics, e.g. for embodied agents or to train 3D generative models. However, so far m…
Animal3D: A Comprehensive Dataset of 3D Animal Pose and Shape
Jiacong Xu, Yi Zhang, Jiawei Peng +17
Accurately estimating the 3D pose and shape is an essential step towards understanding animal behavior, and can potentially benefit many downstream applications, such as wildlife c…
NOVUM: Neural Object Volumes for Robust Object Classification
Artur Jesslen, Guofeng Zhang, Angtian Wang +3
Discriminative models for object classification typically learn image-based representations that do not capture the compositional and 3D nature of objects. In this work, we show th…
OOD-CV-v2: An extended Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images
Bingchen Zhao, Jiahao Wang, Wufei Ma +7
Enhancing the robustness of vision algorithms in real-world scenarios is challenging. One reason is that existing robustness benchmarks are limited, as they either rely on syntheti…