85 citations · 356 across the 26 of their papers we have counts for
32 papers
EMPERROR: A Flexible Generative Perception Error Model for Probing Self-Driving Planners
Niklas Hanselmann, Simon Doll, Marius Cordts +2
To handle the complexities of real-world traffic, learning planners for self-driving from data is a promising direction. While recent approaches have shown great progress, they typ…
DualAD: Disentangling the Dynamic and Static World for End-to-End Driving
Simon Doll, Niklas Hanselmann, Lukas Schneider +4
State-of-the-art approaches for autonomous driving integrate multiple sub-tasks of the overall driving task into a single pipeline that can be trained in an end-to-end fashion by p…
Iterative Cluster Harvesting for Wafer Map Defect Patterns
Alina Pleli, Simon Baeuerle, Michel Janus +3
Unsupervised clustering of wafer map defect patterns is challenging because the appearance of certain defect patterns varies significantly. This includes changing shape, location,…
Dual-Query Multiple Instance Learning for Dynamic Meta-Embedding based Tumor Classification
Simon Holdenried-Krafft, Peter Somers, Ivonne A. Montes-Majarro +4
Whole slide image (WSI) assessment is a challenging and crucial step in cancer diagnosis and treatment planning. WSIs require high magnifications to facilitate sub-cellular analysi…
CANDID: Correspondence AligNment for Deep-burst Image Denoising
Arijit Mallick, Raphael Braun, Hendrik PA Lensch
With the advent of mobile phone photography and point-and-shoot cameras, deep-burst imaging is widely used for a number of photographic effects such as depth of field, super-resolu…
S.T.A.R.-Track: Latent Motion Models for End-to-End 3D Object Tracking with Adaptive Spatio-Temporal Appearance Representations
Simon Doll, Niklas Hanselmann, Lukas Schneider +3
Following the tracking-by-attention paradigm, this paper introduces an object-centric, transformer-based framework for tracking in 3D. Traditional model-based tracking approaches i…
Visual Grounding of Inter-lingual Word-Embeddings
Wafaa Mohammed, Hassan Shahmohammadi, Hendrik P. A. Lensch +1
Visual grounding of Language aims at enriching textual representations of language with multiple sources of visual knowledge such as images and videos. Although visual grounding is…
Diverse Video Captioning by Adaptive Spatio-temporal Attention
Zohreh Ghaderi, Leonard Salewski, Hendrik P. A. Lensch
To generate proper captions for videos, the inference needs to identify relevant concepts and pay attention to the spatial relationships between them as well as to the temporal dev…
How direct is the link between words and images?
Hassan Shahmohammadi, Maria Heitmeier, Elnaz Shafaei-Bajestan +2
Current word embedding models despite their success, still suffer from their lack of grounding in the real world. In this line of research, Gunther et al. 2022 proposed a behaviora…
Language with Vision: a Study on Grounded Word and Sentence Embeddings
Hassan Shahmohammadi, Maria Heitmeier, Elnaz Shafaei-Bajestan +2
Grounding language in vision is an active field of research seeking to construct cognitively plausible word and sentence representations by incorporating perceptual knowledge from…
SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collections
Mark Boss, Andreas Engelhardt, Abhishek Kar +5
Inverse rendering of an object under entirely unknown capture conditions is a fundamental challenge in computer vision and graphics. Neural approaches such as NeRF have achieved ph…
CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations
Leonard Salewski, A. Sophia Koepke, Hendrik P. A. Lensch +1
Providing explanations in the context of Visual Question Answering (VQA) presents a fundamental problem in machine learning. To obtain detailed insights into the process of generat…
Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decomposition
Mark Boss, Varun Jampani, Raphael Braun +3
Decomposing a scene into its shape, reflectance and illumination is a fundamental problem in computer vision and graphics. Neural approaches such as NeRF have achieved remarkable s…
Learning Zero-Shot Multifaceted Visually Grounded Word Embeddings via Multi-Task Training
Hassan Shahmohammadi, Hendrik P. A. Lensch, R. Harald Baayen
Language grounding aims at linking the symbolic representation of language (e.g., words) into the rich perceptual knowledge of the outside world. The general approach is to embed b…
NeRD: Neural Reflectance Decomposition from Image Collections
Mark Boss, Raphael Braun, Varun Jampani +3
Decomposing a scene into its shape, reflectance, and illumination is a challenging but important problem in computer vision and graphics. This problem is inherently more challengin…
Learning to Adapt Multi-View Stereo by Self-Supervision
Arijit Mallick, Jörg Stückler, Hendrik Lensch
3D scene reconstruction from multiple views is an important classical problem in computer vision. Deep learning based approaches have recently demonstrated impressive reconstructio…
Inferring, Predicting, and Denoising Causal Wave Dynamics
Matthias Karlbauer, Sebastian Otte, Hendrik P. A. Lensch +3
The novel DISTributed Artificial neural Network Architecture (DISTANA) is a generative, recurrent graph convolution neural network. It implements a grid or mesh of locally paramete…
Latent State Inference in a Spatiotemporal Generative Model
Matthias Karlbauer, Tobias Menge, Sebastian Otte +4
Knowledge about the hidden factors that determine particular system dynamics is crucial for both explaining them and pursuing goal-directed interventions. Inferring these factors f…
Two-shot Spatially-varying BRDF and Shape Estimation
Mark Boss, Varun Jampani, Kihwan Kim +2
Capturing the shape and spatially-varying appearance (SVBRDF) of an object from images is a challenging task that has applications in both computer vision and graphics. Traditional…
A Distributed Neural Network Architecture for Robust Non-Linear Spatio-Temporal Prediction
Matthias Karlbauer, Sebastian Otte, Hendrik P. A. Lensch +3
We introduce a distributed spatio-temporal artificial neural network architecture (DISTANA). It encodes mesh nodes using recurrent, neural prediction kernels (PKs), while neural tr…
GGNN: Graph-based GPU Nearest Neighbor Search
Fabian Groh, Lukas Ruppert, Patrick Wieschollek +1
Approximate nearest neighbor (ANN) search in high dimensions is an integral part of several computer vision systems and gains importance in deep learning with explicit memory repre…
Single Image BRDF Parameter Estimation with a Conditional Adversarial Network
Mark Boss, Hendrik P. A. Lensch
Creating plausible surfaces is an essential component in achieving a high degree of realism in rendering. To relieve artists, who create these surfaces in a time-consuming, manual…
Flex-Convolution (Million-Scale Point-Cloud Learning Beyond Grid-Worlds)
Fabian Groh, Patrick Wieschollek, Hendrik P. A. Lensch
Traditional convolution layers are specifically designed to exploit the natural data representation of images -- a fixed and regular grid. However, unstructured data like 3D point…
Learning Blind Motion Deblurring
Patrick Wieschollek, Michael Hirsch, Bernhard Schölkopf +1
As handheld video cameras are now commonplace and available in every smartphone, images and videos can be recorded almost everywhere at anytime. However, taking a quick shot freque…