Technische Universität München

∙ München , Germany ∙ 0 followers

Technical University of Munich is a research university with campuses in Munich, Garching and Freising-Weihenstephan.

A Comparative Study between Bayesian and Frequentist Neural Networks for Remaining Useful Life Estimation in Condition-Based Maintenance

In the last decade, deep learning (DL) has outperformed model-based and ...
11/14/2019 ∙ by Luca Della Libera, et al. ∙ 64 ∙ share

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Autolabeling 3D Objects with Differentiable Rendering of SDF Shape Priors

We present an automatic annotation pipeline to recover 9D cuboids and 3D...
11/26/2019 ∙ by Sergey Zakharov, et al. ∙ 23 ∙ share

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Adversarial Convolutional Networks with Weak Domain-Transfer for Multi-Sequence Cardiac MR Images Segmentation

Analysis and modeling of the ventricles and myocardium are important in ...
08/25/2019 ∙ by Jingkun Chen, et al. ∙ 21 ∙ share

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WeatherBench: A benchmark dataset for data-driven weather forecasting

Data-driven approaches, most prominently deep learning, have become powe...
02/02/2020 ∙ by Stephan Rasp, et al. ∙ 19 ∙ share

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Predictive Collective Variable Discovery with Deep Bayesian Models

Extending spatio-temporal scale limitations of models for complex atomis...
09/18/2018 ∙ by Markus Schöberl, et al. ∙ 18 ∙ share

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clDice – a Topology-Preserving Loss Function for Tubular Structure Segmentation

Accurate segmentation of tubular, network-like structures, such as vesse...
03/16/2020 ∙ by Suprosanna, et al. ∙ 18 ∙ share

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Hierarchical Feature Embedding for Attribute Recognition

Attribute recognition is a crucial but challenging task due to viewpoint...
05/23/2020 ∙ by Jie Yang, et al. ∙ 18 ∙ share

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Generalized Zero and Few-Shot Transfer for Facial Forgery Detection

We propose Deep Distribution Transfer(DDT), a new transfer learning appr...
06/21/2020 ∙ by Shivangi Aneja, et al. ∙ 18 ∙ share

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e-UDA: Efficient Unsupervised Domain Adaptation for Cross-Site Medical Image Segmentation

Domain adaptation in healthcare data is a potentially critical component...
01/25/2020 ∙ by Hongwei Li, et al. ∙ 17 ∙ share

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ERA: A Dataset and Deep Learning Benchmark for Event Recognition in Aerial Videos

Along with the increasing use of unmanned aerial vehicles (UAVs), large ...
01/30/2020 ∙ by Lichao Mou, et al. ∙ 16 ∙ share

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A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection

Object detection in camera images, using deep learning has been proven s...
05/15/2020 ∙ by Felix Nobis, et al. ∙ 16 ∙ share

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Learning a Neural Solver for Multiple Object Tracking

Graphs offer a natural way to formulate Multiple Object Tracking (MOT) w...
12/16/2019 ∙ by Guillem Brasó, et al. ∙ 15 ∙ share

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FutureGAN: Anticipating the Future Frames of Video Sequences using Spatio-Temporal 3d Convolutions in Progressively Growing Autoencoder GANs

We propose a new Autoencoder GAN model, FutureGAN, that predicts future ...
10/02/2018 ∙ by Sandra Aigner, et al. ∙ 14 ∙ share

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SteReFo: Efficient Image Refocusing with Stereo Vision

Whether to attract viewer attention to a particular object, give the imp...
09/29/2019 ∙ by Benjamin Busam, et al. ∙ 14 ∙ share

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Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational Approach

A novel approach towards depth map super-resolution using multi-view unc...
12/13/2019 ∙ by Lu Sang, et al. ∙ 14 ∙ share

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MultiDepth: Single-Image Depth Estimation via Multi-Task Regression and Classification

We introduce MultiDepth, a novel training strategy and convolutional neu...
07/25/2019 ∙ by Lukas Liebel, et al. ∙ 12 ∙ share

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Planning from Images with Deep Latent Gaussian Process Dynamics

Planning is a powerful approach to control problems with known environme...
05/07/2020 ∙ by Nathanael Bosch, et al. ∙ 12 ∙ share

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CorNet: Generic 3D Corners for 6D Pose Estimation of New Objects without Retraining

We present a novel approach to the detection and 3D pose estimation of o...
08/29/2019 ∙ by Giorgia Pitteri, et al. ∙ 11 ∙ share

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HistoNet: Predicting size histograms of object instances

We propose to predict histograms of object sizes in crowded scenes direc...
12/11/2019 ∙ by Kishan Sharma, et al. ∙ 11 ∙ share

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Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learning

In this paper we introduce the first reinforcement learning (RL) based r...
03/30/2020 ∙ by Hannes Hase, et al. ∙ 11 ∙ share

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Parsing Geometry Using Structure-Aware Shape Templates

Real-life man-made objects often exhibit strong and easily-identifiable ...
08/03/2018 ∙ by Vignesh Ganapathi-Subramanian, et al. ∙ 10 ∙ share

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An Optical Flow-Based Approach for Minimally-Divergent Velocimetry Data Interpolation

Three-dimensional (3D) biomedical image sets are often acquired with in-...
12/20/2018 ∙ by Berkay Kanberoglu, et al. ∙ 10 ∙ share

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Tracking without bells and whistles

The problem of tracking multiple objects in a video sequence poses sever...
03/13/2019 ∙ by Philipp Bergmann, et al. ∙ 10 ∙ share

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Scalable Deep Learning on Distributed Infrastructures: Challenges, Techniques and Tools

Deep Learning (DL) has had an immense success in the recent past, leadin...
03/27/2019 ∙ by Ruben Mayer, et al. ∙ 10 ∙ share

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A Review on Neural Network Models of Schizophrenia and Autism Spectrum Disorder

This survey presents the most relevant neural network models of autism s...
06/24/2019 ∙ by Pablo Lanillos, et al. ∙ 9 ∙ share

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Force-Ultrasound Fusion: Bringing Spine Robotic-US to the Next "Level"

Spine injections are commonly performed in several clinical procedures. ...
02/26/2020 ∙ by Maria Tirindelli, et al. ∙ 9 ∙ share

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Spatiotemporal-Aware Augmented Reality: Redefining HCI in Image-Guided Therapy

Suboptimal interaction with patient data and challenges in mastering 3D ...
03/04/2020 ∙ by Javad Fotouhi, et al. ∙ 9 ∙ share

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Indirect and Direct Training of Spiking Neural Networks for End-to-End Control of a Lane-Keeping Vehicle

Building spiking neural networks (SNNs) based on biological synaptic pla...
03/10/2020 ∙ by Zhenshan Bing, et al. ∙ 9 ∙ share

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Spatio-temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation

Segmentation of Multiple Sclerosis (MS) lesions in longitudinal brain MR...
04/07/2020 ∙ by Stefan Denner, et al. ∙ 9 ∙ share

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Recurrently Exploring Class-wise Attention in A Hybrid Convolutional and Bidirectional LSTM Network for Multi-label Aerial Image Classification

Aerial image classification is of great significance in remote sensing c...
07/30/2018 ∙ by Yuansheng Hua, et al. ∙ 8 ∙ share

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CVPR19 Tracking and Detection Challenge: How crowded can it get?

Standardized benchmarks are crucial for the majority of computer vision ...
06/10/2019 ∙ by Patrick Dendorfer, et al. ∙ 8 ∙ share

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Object-Driven Multi-Layer Scene Decomposition From a Single Image

We present a method that tackles the challenge of predicting color and d...
08/26/2019 ∙ by Helisa Dhamo, et al. ∙ 8 ∙ share

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Self6D: Self-Supervised Monocular 6D Object Pose Estimation

Estimating the 6D object pose is a fundamental problem in computer visio...
04/14/2020 ∙ by Gu Wang, et al. ∙ 8 ∙ share

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Decision Support for Intoxication Prediction Using Graph Convolutional Networks

Every day, poison control centers (PCC) are called for immediate classif...
05/02/2020 ∙ by Hendrik Burwinkel, et al. ∙ 8 ∙ share

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Cross-Task Transfer for Multimodal Aerial Scene Recognition

Aerial scene recognition is a fundamental task in remote sensing and has...
05/18/2020 ∙ by Di Hu, et al. ∙ 8 ∙ share

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From Planes to Corners: Multi-Purpose Primitive Detection in Unorganized 3D Point Clouds

We propose a new method for segmentation-free joint estimation of orthog...
01/21/2020 ∙ by Christiane Sommer, et al. ∙ 7 ∙ share

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Red-GAN: Attacking class imbalance via conditioned generation. Yet another medical imaging perspective

Exploiting learning algorithms under scarce data regimes is a limitation...
04/22/2020 ∙ by Ahmad B Qasim, et al. ∙ 7 ∙ share

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Multi-Source Neural Variational Inference

Learning from multiple sources of information is an important problem in...
11/11/2018 ∙ by Richard Kurle, et al. ∙ 6 ∙ share

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Temporally Coherent GANs for Video Super-Resolution (TecoGAN)

Adversarial training has been highly successful in the context of image ...
11/23/2018 ∙ by Mengyu Chu, et al. ∙ 6 ∙ share

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Deferred Neural Rendering: Image Synthesis using Neural Textures

The modern computer graphics pipeline can synthesize images at remarkabl...
04/28/2019 ∙ by Justus Thies, et al. ∙ 6 ∙ share

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Towards Unsupervised Image Captioning with Shared Multimodal Embeddings

Understanding images without explicit supervision has become an importan...
08/25/2019 ∙ by Iro Laina, et al. ∙ 6 ∙ share

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Deep Model-Based 6D Pose Refinement in RGB

We present a novel approach for model-based 6D pose refinement in color ...
10/07/2018 ∙ by Fabian Manhardt, et al. ∙ 5 ∙ share

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Real-Time Driver State Monitoring Using a CNN Based Spatio-Temporal Approach

Many road accidents occur due to distracted drivers. Today, driver monit...
07/18/2019 ∙ by Neslihan Köse, et al. ∙ 5 ∙ share

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Omnidirectional DSO: Direct Sparse Odometry with Fisheye Cameras

We propose a novel real-time direct monocular visual odometry for omnidi...
08/08/2018 ∙ by Hidenobu Matsuki, et al. ∙ 2 ∙ share

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SSD-6D: Making RGB-based 3D detection and 6D pose estimation great again

We present a novel method for detecting 3D model instances and estimatin...
11/27/2017 ∙ by Wadim Kehl, et al. ∙ 1 ∙ share

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A Novel Multi-Detector Fusion Framework for Multi-Object Tracking

In order to track all persons in a scene, the tracking-by-detection para...
05/23/2017 ∙ by Roberto Henschel, et al. ∙ 1 ∙ share

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Tracking with multi-level features

We present a novel formulation of the multiple object tracking problem w...
07/25/2016 ∙ by Roberto Henschel, et al. ∙ 1 ∙ share

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Motion Fused Frames: Data Level Fusion Strategy for Hand Gesture Recognition

Acquiring spatio-temporal states of an action is the most crucial step f...
04/19/2018 ∙ by Okan Köpüklü, et al. ∙ 1 ∙ share

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Peeking Behind Objects: Layered Depth Prediction from a Single Image

While conventional depth estimation can infer the geometry of a scene fr...
07/23/2018 ∙ by Helisa Dhamo, et al. ∙ 1 ∙ share

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Convolutional Neural Networks with Layer Reuse

A convolutional layer in a Convolutional Neural Network (CNN) consists o...
01/28/2019 ∙ by Okan Köpüklü, et al. ∙ 1 ∙ share

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