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07/18/2023
Unsupervised Deep Graph Matching Based on Cycle Consistency
We contribute to the sparsely populated area of unsupervised deep graph ...
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03/17/2023
Finding Competence Regions in Domain Generalization
We propose a "learning to reject" framework to address the problem of si...
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02/25/2023
BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects
We present the evaluation methodology, datasets and results of the BOP C...
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07/01/2022
A Comparative Study of Graph Matching Algorithms in Computer Vision
The graph matching optimization problem is an essential component for ma...
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03/30/2022
Towards Multimodal Depth Estimation from Light Fields
Light field applications, especially light field rendering and depth est...
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02/18/2022
Spatio-Temporal Outdoor Lighting Aggregation on Image Sequences using Transformer Networks
In this work, we focus on outdoor lighting estimation by aggregating ind...
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01/31/2022
Exoplanet Characterization using Conditional Invertible Neural Networks
The characterization of an exoplanet's interior is an inverse problem, w...
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12/02/2021
Neural Head Avatars from Monocular RGB Videos
We present Neural Head Avatars, a novel neural representation that expli...
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10/19/2021
Self-Supervised Object Detection via Generative Image Synthesis
We present SSOD, the first end-to-end analysis-by synthesis framework wi...
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09/01/2021
On the Limits of Pseudo Ground Truth in Visual Camera Re-localisation
Benchmark datasets that measure camera pose accuracy have driven progres...
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05/05/2021
Conditional Invertible Neural Networks for Diverse Image-to-Image Translation
We introduce a new architecture called a conditional invertible neural n...
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01/28/2021
Fusion Moves for Graph Matching
We contribute to approximate algorithms for the quadratic assignment pro...
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01/26/2021
Benchmarking Invertible Architectures on Inverse Problems
Recent work demonstrated that flow-based invertible neural networks are ...
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12/15/2020
Representing Ambiguity in Registration Problems with Conditional Invertible Neural Networks
Image registration is the basis for many applications in the fields of m...
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11/10/2020
Invertible Neural Networks for Uncertainty Quantification in Photoacoustic Imaging
Multispectral photoacoustic imaging (PAI) is an emerging imaging modalit...
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10/14/2020
Learning Robust Models Using The Principle of Independent Causal Mechanisms
Standard supervised learning breaks down under data distribution shift. ...
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10/12/2020
Increasing the Robustness of Semantic Segmentation Models with Painting-by-Numbers
For safety-critical applications such as autonomous driving, CNNs have t...
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09/15/2020
BOP Challenge 2020 on 6D Object Localization
This paper presents the evaluation methodology, datasets, and results of...
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07/29/2020
Generative Classifiers as a Basis for Trustworthy Computer Vision
With the maturing of deep learning systems, trustworthiness is becoming ...
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06/29/2020
Intrinsic Autoencoders for Joint Neural Rendering and Intrinsic Image Decomposition
Neural rendering techniques promise efficient photo-realistic image synt...
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06/13/2020
Split-Merge Pooling
There are a variety of approaches to obtain a vast receptive field with ...
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04/16/2020
MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models
Dense, discrete Graphical Models with pairwise potentials are a powerful...
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04/16/2020
Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization
We consider the maximum-a-posteriori inference problem in discrete graph...
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04/14/2020
A Primal-Dual Solver for Large-Scale Tracking-by-Assignment
We propose a fast approximate solver for the combinatorial problem known...
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04/03/2020
Self-Supervised Viewpoint Learning From Image Collections
Training deep neural networks to estimate the viewpoint of objects requi...
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02/27/2020
Visual Camera Re-Localization from RGB and RGB-D Images Using DSAC
We describe a learning-based system that estimates the camera position a...
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01/17/2020
Exact Information Bottleneck with Invertible Neural Networks: Getting the Best of Discriminative and Generative Modeling
The Information Bottleneck (IB) principle offers a unified approach to m...
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01/14/2020
Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)
A central question of representation learning asks under which condition...
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01/08/2020
CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus
We present a robust estimator for fitting multiple parametric models of ...
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12/02/2019
Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task
We address a core problem of computer vision: Detection and description ...
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11/05/2019
Out of distribution detection for intra-operative functional imaging
Multispectral optical imaging is becoming a key tool in the operating ro...
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09/19/2019
Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift
We propose a method for depth estimation from light field data, based on...
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08/14/2019
Benchmarking the Robustness of Semantic Segmentation Models
When designing a semantic segmentation module for a practical applicatio...
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08/07/2019
Expert Sample Consensus Applied to Camera Re-Localization
Fitting model parameters to a set of noisy data points is a common probl...
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07/04/2019
Guided Image Generation with Conditional Invertible Neural Networks
In this work, we address the task of natural image generation guided by ...
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05/25/2019
HINT: Hierarchical Invertible Neural Transport for General and Sequential Bayesian inference
In this paper, we introduce Hierarchical Invertible Neural Transport (HI...
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05/10/2019
Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses
We present Neural-Guided RANSAC (NG-RANSAC), an extension to the classic...
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03/08/2019
Uncertainty-aware performance assessment of optical imaging modalities with invertible neural networks
Purpose: Optical imaging is evolving as a key technique for advanced sen...
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10/23/2018
CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation
State of the art methods for semantic image segmentation are trained in ...
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10/09/2018
A Summary of the 4th International Workshop on Recovering 6D Object Pose
This document summarizes the 4th International Workshop on Recovering 6D...
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09/12/2018
Geometric Image Synthesis
The task of generating natural images from 3D scenes has been a long sta...
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08/24/2018
BOP: Benchmark for 6D Object Pose Estimation
We propose a benchmark for 6D pose estimation of a rigid object from a s...
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08/14/2018
Analyzing Inverse Problems with Invertible Neural Networks
In many tasks, in particular in natural science, the goal is to determin...
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04/17/2018
Deep Object Co-Segmentation
This work presents a deep object co-segmentation (DOCS) approach for seg...
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01/03/2018
Panoptic Segmentation
We propose and study a novel 'Panoptic Segmentation' (PS) task. Panoptic...
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12/05/2017
The Best of Both Worlds: Learning Geometry-based 6D Object Pose Estimation
We address the task of estimating the 6D pose of known rigid objects, fr...
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11/28/2017
Learning Less is More - 6D Camera Localization via 3D Surface Regression
Popular research areas like autonomous driving and augmented reality hav...
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08/04/2017
Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenes
The success of deep learning in computer vision is based on availability...
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02/26/2017
Analyzing Modular CNN Architectures for Joint Depth Prediction and Semantic Segmentation
This paper addresses the task of designing a modular neural network arch...
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02/21/2017
