Anas Gouda, Max Schwarz, Christopher Reining, Sven Behnke, and Alice Kirchheim:
Learning Embeddings with Centroid Triplet Loss for Object Identification in Robotic Grasping
Accepted for IEEE 20th International Conference on Automation Science and Engineering (CASE),
Bari, Italy, August 2024.
Lennard Bodden, Duc Bach Ha, Franziska Schwaiger, Lars Kreuzberg,
and Sven Behnke:
Spiking CenterNet: A
Distillation-Boosted Spiking Neural Network for Object Detection
Accepted for International Joint Conference on Neural Networks (IJCNN),
Yokohama, Japan, July 2024.
Andre Rochow, Max Schwarz, and Sven Behnke:
FSRT:
Facial Scene Representation Transformer for Face Reenactment from
Factorized Appearance, Head-pose, and Facial Expression Features
Accepted for IEEE/CVF Conference on Computer Vision and Pattern
Recognition (CVPR), Seattle, USA, June 2024.
[Video] [Project Page]
Helin Cao and Sven Behnke:
SLCF-Net: Sequential LiDAR-Camera
Fusion for Semantic Scene
Completion using a 3D Recurrent U-Net
In Proceedings of IEEE International Conference on Robotics and Automation
(ICRA), Yokohama, Japan, May 2024.
[Video]
Malte Mosbach and Sven Behnke:
Grasp Anything: Combining
Teacher-Augmented Policy Gradient
Learning with Instance Segmentation to Grasp Arbitrary Objects
In Proceedings of IEEE International Conference on Robotics and
Automation
(ICRA), Yokohama, Japan, May 2024.
[Video] [Project Page]
Arul Selvam Periyasamy and Sven Behnke:
MOTPose: Multi-object 6D
Pose Estimation for Dynamic Video Sequences using Attention-based
Temporal Fusion
In Proceedings of IEEE International Conference on Robotics and
Automation
(ICRA), Yokohama, Japan, May 2024.
[Video] [Project Page]
Arul Selvam Periyasamy, Vladimir Tsaturyan, and Sven Behnke:
Efficient Multi-Object Pose
Estimation using Multi-Resolution Deformable Attention and Query
Aggregation
In Proceedings of IEEE International Conference on Robotic Computing
(IRC),
Laguna Hills, USA,
December 2023.
Tobias Selzner, Jannis Horn, Magdalena Landl, Andreas Pohlmeier,
Dirk
Helmrich, Katrin Huber, Jan Vanderborght, Harry Vereecken, Sven Behnke,
and Andrea Schnepf:
3D U-Net Segmentation Improves
Root System Reconstruction from 3D MRI Images in Automated and Manual
Virtual Reality Work Flows
Plant Phenomics, 2023:5, Article 0076, 2023.
Arul Selvam Periyasamy, Arash Amini, Vladimir Tsaturyan, and Sven
Behnke:
YOLOPose V2: Understanding and
Improving Transformer-based 6D Pose Estimation
Robotics and Autonomous Systems, 168:104490, Elsevier, 2023.
Angel Villar-Corrales, Ismail Wahdan, and Sven Behnke:
Object-centric Video
Prediction via Decoupling of Object Dynamics and Interactions
In Proceedings of IEEE International Conference on Image Processing
(ICIP), pp. 570-574,
Kuala Lumpur, Malaysia, October 2023.
[Video] [Project Page]
Ilyass Taouil, Giulio Turrisi, Daniel Schleich, Victor Barasuol,
Claudio Semini, and Sven Behnke:
Quadrupedal Footstep Planning using Learned Motion Models of a
Black-Box Controller
In Proceedings of IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS), Detroit, USA, October 2023.
[Video]
Andre Rochow, Max Schwarz, and Sven Behnke:
Attention-Based VR Facial
Animation with Visual Mouth Camera Guidance for Immersive Telepresence
Avatars
In Proceedings of IEEE/RSJ International Conference on
Intelligent
Robots and Systems (IROS), Detroit, USA, October 2023.
[Videos]
Bastian Pätzold, Andre Rochow, Michael Schreiber, Raphael
Memmesheimer, Christian Lenz, Max Schwarz, and Sven Behnke:
Audio-based Roughness Sensing
and Tactile Feedback for Haptic Perception in Telepresence
In Proceedings of IEEE International Conference on Systems, Man, and
Cybernetics (SMC), Oahu, Hawaii, USA, October 2023.
[Video] [Project Page]
Malte Mosbach and Sven Behnke:
Learning Generalizable Tool
Use with Non-rigid Grasp-pose Registration
In Proceedings of IEEE 19th International Conference on Automation
Science
and Engineering (CASE), Auckland, New Zealand, August 2023.
Best Conference Paper Award.
[Project
Page]
Dmytro Pavlichenko and Sven Behnke:
Deep Reinforcement Learning
of Dexterous Pre-grasp Manipulation for Human-like Functional
Categorical Grasping
In Proceedings of IEEE 19th International Conference on Automation
Science and
Engineering (CASE), Auckland, New Zealand, August 2023.
[Video]
Radu Alexandru Rosu and Sven Behnke:
PermutoSDF:
Fast Multi-View Reconstruction with
Implicit Surfaces using Permutohedral Lattices
In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern
Recognition (CVPR), Vancouver, Canada, June 2023.
[Project Page]
Simon Bultmann, Raphael Memmesheimer, and Sven Behnke:
External Camera-based Mobile Robot
Pose Estimation for Collaborative Perception with Smart Edge Sensors
In Proceedings of IEEE International Conference on Robotics and
Automation
(ICRA), London, UK, June 2023.
[Video]
Marcin Namysl,
Alexander Esser,
Sven Behnke, and
Joachim Köhler:
Flexible Hybrid Table Recognition
and Semantic Interpretation System
SN Computer Science, 4(3):246, March 2023.
Simon Bultmann, Jan Quenzel, and Sven Behnke:
Real-Time Multi-Modal
Semantic Fusion on Unmanned Aerial Vehicles with Label Propagation for
Cross-Domain Adaptation
Robotics and Autonomous Systems, 159:104286, Elsevier, January 2023.
Arul Selvam Periyasamy, Luis Denninger, and Sven Behnke:
Learning Implicit
Probability Distribution Functions for Symmetric Orientation Estimation
from RGB Images Without Pose Labels
In Proceedings of 6th IEEE International Conference on Robotic
Computing (IRC),
Naples, Italy, December 2022.
Malte Mosbach and Sven Behnke:
Efficient
Representations of Object Geometry for Reinforcement Learning of
Interactive Grasping Policies
In Proceedings of 6th IEEE International Conference on Robotic
Computing (IRC),
Naples, Italy, December 2022.
[Project
Page]
Julian Hau, Simon Bultmann, and Sven Behnke:
Object-level 3D Semantic
Mapping using a Network of Smart Edge Sensors
In Proceedings of 6th IEEE International Conference on Robotic
Computing (IRC),
Naples, Italy, December 2022.
Dmytro Pavlichenko and Sven Behnke:
Flexible-Joint
Manipulator Trajectory Tracking with Two-Stage Learned Model utilizing
a Hardwired Forward Dynamics Prediction
International Journal of Semantic Computing (IJSC), 16(3):403 - 423,
World
Scientific, November 2022.
Malte Mosbach, Kara Moraw, and Sven Behnke:
Accelerating Interactive
Human-like Manipulation Learning with GPU-based Simulation and
High-quality Demonstrations
In Proceedings of IEEE-RAS 21st International Conference on Humanoid
Robots (Humanoids), Ginowan, Okinawa, Japan, November 2022.
[Project
Page]
Angel Villar-Corrales, Ani Karapetyan, Andreas Boltres, and
Sven Behnke:
MSPred: Video Prediction
at Multiple
Spatio-Temporal Scales with Hierarchical
Recurrent Networks
In Proceedings of 33rd British Machine Vision Conference (BMVC),
London, UK, November 2022.
[Project Page] [Supplementary
Material]
Radu Alexandru Rosu, Shunsuke Saito, Ziyan Wang, Chenglei Wu,
Sven Behnke, and Giljoo Nam:
Neural Strands: Learning
Hair Geometry and Appearance from Multi-View Images
In Proceedings of European Conference on Computer Vision (ECCV), Tel
Aviv, Israel, October 2022.
[Project
Page] [Supplementary
Material]
Andre Rochow, Max Schwarz, Michael Schreiber, and Sven Behnke:
VR Facial Animation for
Immersive Telepresence Avatars
In Proceedings of IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS), Kyoto, Japan, October 2022.
[Videos]
Hafez Farazi and Sven Behnke:
Intention-Aware
Frequency Domain Transformer Networks for Video Prediction
In Proceedings of 31st International Conference on Artificial Neural
Networks (ICANN), Bristol, UK, September 2022.
[Video]
Benedikt T. Imbusch, Max Schwarz, and Sven Behnke:
Synthetic-to-Real Domain
Adaptation using Contrastive Unpaired Translation
In Proceedings of 18th IEEE International Conference on Automation
Science and Engineering (CASE), Mexico City, Mexico, August 2022.
[Video]
Radu Alexandru Rosu and Sven Behnke:
NeuralMVS:
Bridging Multi-View Stereo and Novel View Synthesis
In Proceedings of International Joint Conference on Neural Networks
(IJCNN),
Padova, Italy, July 2022.
[Project
Page]
Martin Link, Max Schwarz, and Sven Behnke:
Predicting
Physical Object Properties from Video
In Proceedings of International Joint Conference on Neural Networks
(IJCNN),
Padova, Italy, July 2022.
Andre Rochow, Max Schwarz, Michael Weinmann, and Sven Behnke:
FaDIV-Syn: Fast
Depth-Independent View Synthesis using Soft Masks and Implicit Blending
In Proceedings of Robotics: Science and Systems (RSS), New York, USA,
June 2022.
[Video] [Supplementary
Material] [Sources]
Arash Amini, Arul Selvam Periyasamy, and Sven Behnke:
YOLOPose: Transformer-based Multi-Object 6D Pose Estimation using
Keypoint Regression
In Proceedings of 17th International Conference on Intelligent
Autonomous
Systems (IAS), Zagreb, Croatia, June 2022.
Best Paper Award.
Simon Bultmann and Sven Behnke:
3D Semantic Scene Perception using Distributed Smart Edge Sensors
In Proceedings of 17th International Conference on Intelligent
Autonomous
Systems (IAS), Zagreb, Croatia, June 2022.
[Video]
Peer Schütt, Radu Alexandru Rosu, and Sven Behnke:
Abstract Flow for
Temporal Semantic Segmentation on the Permutohedral Lattice
In Proceedings of IEEE International Conference on Robotics and
Automation (ICRA), Philadelphia (PA), USA, May 2022.
[Video]
Dmytro Pavlichenko and Sven Behnke:
Real-Robot Deep
Reinforcement Learning: Improving Trajectory Tracking of Flexible-Joint
Manipulator with Reference Correction
In Proceedings of IEEE International Conference on Robotics and
Automation (ICRA), Philadelphia (PA), USA, May 2022.
[Videos]
Angel Villar-Corrales and Sven Behnke:
Unsupervised Image
Decomposition with Phase-Correlation Networks
In Proceedings of 17th International Conference on Computer Vision
Theory and Applications (VISAPP), February 2022.
[Source]
Marcin Namysl, Alexander M. Esser, Sven Behnke, and Joachim
Köhler:
Flexible Table
Recognition and Semantic Interpretation System
In Proceedings of 17th International Conference on Computer Vision
Theory and Applications (VISAPP), February 2022.
Finalist for Best Industrial Paper Award.
Arul Selvam Periyasamy, Catherine Capellen, Max Schwarz, and
Sven Behnke:
ConvPoseCNN2: Prediction
and Refinement of Dense 6D Object Pose
In:
Imaging and Computer Graphics Theory and Applications. VISIGRAPP 2020.
Communications in Computer and Information Science (CCIS), vol. 1474,
pp. 353-371, Springer, January 2022.
Dmytro Pavlichenko and Sven Behnke:
Flexible-Joint
Manipulator Trajectory Tracking with Learned Two-Stage Model employing
One-Step Future Prediction
In Proceedings of 5th IEEE International Conference on Robotic
Computing (IRC), Taichung, Taiwan, November 2021.
[Video]
Radu Alexandru Rosu, Peer Schuett, Jan Quenzel, and Sven
Behnke:
LatticeNet: Fast
Spatio-Temporal Point Cloud Segmentation Using Permutohedral Lattices
Autonomous Robots, Springer, October 2021.
Hafez Farazi, Jan Nogga, and Sven Behnke:
Semantic
Prediction: Which One Should Come First, Recognition or Prediction?
In Proceedings of 29th European Symposium on Artificial Neural
Networks, Computational Intelligence and Machine Learning (ESANN),
October 2021.
Malte Mosbach and Sven Behnke:
Fourier-based
Video Prediction through Relational Object Motion
In Proceedings of 29th European Symposium on Artificial Neural
Networks, Computational Intelligence and Machine Learning (ESANN),
October 2021.
[Source]
Arash Amini, Arul Selvam Periyasamy, and Sven Behnke:
T6D-Direct: Transformers
for Multi-Object 6D Pose Direct Regression
In Proceedings of DAGM German Conference on Pattern Recognition (GCPR),
Bonn, Germany, September 2021.
[Video]
Andre Brandenburger, Diego Rodriguez, and Sven Behnke:
Mapless Humanoid
Navigation Using Learned Latent Dynamics
In Proceedings of IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS), Prague, Czech Republic, September 2021.
[Video]
Simon Bultmann, Jan Quenzel, and Sven Behnke:
Real-Time Multi-Modal
Semantic Fusion on Unmanned Aerial Vehicles
In Proceedings of 10th European Conference on Mobile Robots (ECMR),
Bonn, Germany, September 2021.
[Video]
Marcin Namysl, Sven Behnke, and Joachim Köhler:
Empirical Error Modeling
Improves Robustness of Noisy Neural Sequence Labeling
Findings of the Association for Computational Linguistics (Findings of
ACL) 2021: 314-329.
Hafez Farazi, Jan Nogga, and Sven Behnke:
Local Frequency Domain
Transformer Networks for Video Prediction
In Proceedings of International Joint Conference on Neural Networks
(IJCNN), July 2021.
[Video]
[Source]
Simon Bultmann and Sven Behnke:
Real-Time Multi-View 3D
Human Pose Estimation using Semantic Feedback to Smart Edge Sensors
In Proceedings of Robotics: Science and Systems (RSS), July 2021.
[Video] [Source]
Moritz Zappel, Simon Bultmann, and Sven Behnke:
6D Object Pose
Estimation using Keypoints and Part Affinity Fields
In Proceedings of 24th RoboCup International Symposium, June 2021.
Finalist for Best Paper Award
[Video]
Arash Amini, Hafez Farazi, and Sven Behnke:
Real-time Pose
Estimation from Images for Multiple Humanoid Robots
In Proceedings of 24th RoboCup International Symposium, June 2021.
Finalist for Best Paper Award
[Video]
Diego Rodriguez and Sven Behnke:
DeepWalk:
Omnidirectional
Bipedal Gait by Deep Reinforcement Learning
In Proceedings of IEEE International Conference on Robotics and
Automation (ICRA), Xi'an, China, June 2021.
[Videos]
Radu Alexandru Rosu, Peer Schütt, Jan Quenzel, and Sven Behnke:
LatticeNet: Fast Point
Cloud Segmentation Using Permutohedral Lattices
In Proceedings of Robotics: Science and Systems (RSS), Oregon, USA,
July 2020.
[Videos]
Marcin Namysl, Sven Behnke, and Joachim Köhler:
NAT: Noise-Aware
Training for Robust Neural Sequence Labeling
In Proceedings of Annual Conference of the Association for
Computational Linguistics (ACL), Seattle, July 2020.
Max Schwarz and Sven Behnke:
Stillleben:
Realistic Scene Synthesis for Deep Learning in Robotics
In Proceedings of IEEE International Conference on Robotics
and Automation (ICRA), Paris, France, May 2020.
[Source
code] [Presentation
video]
Radu Alexandru Rosu, Jan Quenzel, and Sven Behnke:
Semi-Supervised
Semantic Mapping through Label Propagation with Semantic Texture
Meshes
International Journal of Computer Vision (IJCV) 128(5):1220-1238,
Springer, May 2020.
Hafez Farazi and Sven Behnke:
Motion
Segmentation using Frequency Domain Transformer Networks
In Proceedings of 28th European Symposium on Artificial Neural
Networks, Computational Intelligence and Machine Learning (ESANN),
Bruges, Belgium, April 2020.
[Video]
Moritz Wolter, Angela Yao, and Sven Behnke:
Object-centered Fourier
Motion Estimation and Segment-Transformation Prediction
In Proceedings of 28th European Symposium on Artificial Neural
Networks, Computational Intelligence and Machine Learning (ESANN),
Bruges, Belgium, April 2020.
[Video]
Yi Zhao, Nils Wandel, Magdalena Landl, Andrea Schnepf, and
Sven Behnke:
3D U-Net for
Segmentation of Plant Root MRI Images in Super-Resolution
In Proceedings of 28th European Symposium on Artificial Neural
Networks, Computational Intelligence and Machine Learning (ESANN),
Bruges, Belgium, April 2020.
[Video]
Catherine Capellen, Max Schwarz, and Sven Behnke:
ConvPoseCNN:
Dense Convolutional 6D Object Pose Estimation
In Proceedings of 15th International Conference on Computer Vision
Theory and Applications (VISAPP), pp. 162-172, Valletta, Malta,
February 2020.
Umashankar Deekshith, Nishit Gajjar, Max Schwarz, and Sven Behnke:
Visual
Descriptor
Learning from Monocular Video
In Proceedings of 15th International Conference on Computer
Vision Theory and Applications (VISAPP), p. 444-451, Valletta, Malta,
February 2020.
Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven
Behnke, Cyrill Stachniss, and Jürgen Gall:
SemanticKITTI: A Dataset for
Semantic Scene Understanding of LiDAR Sequences
Accepted for oral presentation at International Conference on Computer
Vision (ICCV), Seoul, Korea, October 2019.
Arul Selvam Periyasamy, Max Schwarz, and Sven Behnke:
Refining 6D Object Pose
Predictionsusing Abstract Render-and-Compare
Accepted for IEEE-RAS International Conference on Humanoid Robots
(Humanoids), Toronto, Canada, October 2019.
Peer Schütt, Max Schwarz, and Sven Behnke:
Semantic
Interaction
in Augmented Reality Environments for Microsoft HoloLens
In Proceedings of European Conference on Mobile Robots (ECMR), Prague,
Czech Republic, September 2019.
[Video}
Daniel Schleich,Tobias Klamt, and Sven Behnke:
Value
Iteration Networks on Multiple Levels of Abstraction
In Proceedings of Robotics: Science and Systems (RSS), Freiburg,
Germany, June 2019.
[Video]
Jörg Wagner, Jan Koehler, Tobias Gindele, Leon Hetzel, Jakob
Thaddaeus Wiedemer, and Sven Behnke:
Interpretable
and
Fine-Grained Visual Explanations for Convolutional Neural Networks
In Proceedings of IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), Long Beach, CA, USA, June 2019.
[Supplementary
Material] [Poster]
Tobias Klamt and Sven Behnke:
Towards
Learning Abstract Representations for Locomotion Planning in
High-dimensional State Spaces
In Proceedings of IEEE International Conference on Robotics and
Automation (ICRA), Montreal, Canada, May 2019.
[Video]
Hafez Farazi and Sven Behnke:
Frequency
Domain Transformer Networks for Video Prediction
In Proceedings of 27th European Symposium on Artificial Neural
Networks, Computational Intelligence and Machine Learning (ESANN),
Bruges, Belgium, April 2019.
[Source code]
Niloofar Azizi, Nils Wandel, and Sven Behnke:
Complex
Valued Gated Auto-encoder for Video Frame Prediction
In Proceedings of 27th European Symposium on Artificial Neural
Networks, Computational Intelligence and Machine Learning (ESANN),
Bruges, Belgium, April 2019.
[Source
code]
Ali Oguz Uzman, Jannis Horn, and Sven Behnke:
Learning
Super-resolution
3D Segmentation of Plant Root MRI Images from Few Examples
In Proceedings of 27th European Symposium on Artificial Neural
Networks, Computational Intelligence and Machine Learning (ESANN),
Bruges, Belgium, April 2019.
Niloofar Azizi, Hafez Farazi, and Sven Behnke:
Location
Dependency
in Video Prediction
In Proceedings of 27th International Conference on Artificial
Neural Networks (ICANN), Rhodes, Greece, October 2018.
Joerg Wagner, Volker Fischer, Michael Herman, and Sven Behnke:
Functionally
Modular and Interpretable Temporal Filtering for Robust
Segmentation
In Proceedings of 29th British Machine Vision Conference (BMVC),
Newcastle, UK, September 2018.
[Supplementary
Material]
Max Schwarz, Christian Lenz, German Martin Garcia, Seongyong Koo,
Arul Selvam Periyasamy, Michael Schreiber, and Sven Behnke:
Fast
Object Learning and Dual-arm Coordination for Cluttered Stowing,
Picking, and Packing
In Proceedings of IEEE International Conference on Robotics and
Automation (ICRA), Brisbane, Australia, May 2018.
[Video]
Jörg Wagner, Volker Fischer, Michael Herman, and Sven Behnke:
Hierarchical Recurrent Filtering for Fully Convolutional DenseNets
In Proceedings of 26th European Symposium on Artificial Neural
Networks, Computational Intelligence and Machine Learning (ESANN),
Bruges, Belgium, April 2018.
Max Schwarz, Anton Milan, Arul Selvam Periyasamy, and Sven Behnke:
RGB-D
Object Detection and Semantic Segmentation for Autonomous
Manipulation in Clutter
International Journal of Robotics Research (IJRR), Sage Publications,
2017.
Seongyong Koo, Grzegorz Ficht, Germán Martín García, Dmytro
Pavlichenko, Martin Raak, and Sven Behnke:
Robolink
Feeder: Reconfigurable Bin-Picking and Feeding with a Lightweight
Cable-Driven Manipulator
In Proceedings of 13th IEEE International Conference on Automation
Science and Engineering (CASE), Xi'an, China, August 2017.
[Video]
Max Schwarz and Sven Behnke:
Data-efficient
Deep Learning for RGB-D Object Perception in Cluttered Bin Picking
In Warehouse Picking Automation Workshop (WPAW), IEEE International
Conference on Robotics and Automation (ICRA), Singapore, May 2017.
Jörg Wagner, Volker Fischer, Michael Herman, and Sven Behnke:
Learning
Semantic
Prediction using Pretrained Deep Feedforward Networks
In Proceedings of 25th European Symposium on Artificial Neural
Networks, Computational Intelligence and Machine Learning (ESANN),
Bruges, Belgium, April 2017.
Mircea Serban Pavel, Hannes Schulz, and Sven Behnke:
Object
class segmentation of RGB-D video using recurrent convolutional
neural networks
Neural Networks, Elsevier, available
online January 2017.
Hannes Schulz:
Learning
Object
Recognition and Object Class Segmentation with Deep Neural Networks
on GPU
Dissertation, Mathematisch-Naturwissenschaftliche Fakultät,
Universität Bonn, 2017.
German Martin Garcia, Farzad Husain, Hannes Schulz, Simone Frintrop,
Carme Torras, and Sven Behnke:
Semantic
Segmentation
Priors for Object Discovery
In Proceedings of: 23rd International Conference on Pattern
Recognition (ICPR), Cancun, Mexico, December 2016.
Farzad Husain, Hannes Schulz, Babette Dellen, Carme Torras, and Sven
Behnke:
Combining
Semantic
and Geometric Features for Object Class Segmentation of Indoor
Scenes
IEEE Robotics and Automation Letters (RA-L) 2(1):49-55, 2016.
Presented at IEEE International Conference on Robotics and Automation
(ICRA), Stockholm, Sweden, May 2016.
Jörg Wagner, Volker Fischer, Michael Herman, and Sven Behnke:
Multispectral
Pedestrian
Detection using Deep Fusion Convolutional Neural Networks
In Proceedings of 24th European Symposium on Artificial Neural
Networks, Computational Intelligence and Machine Learning (ESANN),
Bruges, Belgium, April 2016.
Hannes Schulz, Benedikt Waldvogel, Rasha Sheikh, and Sven Behnke:
CURFIL:
A GPU Library for Image Labeling with Random Forests
In: Computer Vision, Imaging and Computer Graphics Theory and
Applications, volume 598 or Communications in Computer and Information
Science, pp. 416-432, Springer, 2016.
Jörg Stückler, Benedikt Waldvogel, Hannes Schulz, and Sven Behnke:
Dense
Real-Time Mapping of Object-Class Semantics from RGB-D Video
Journal of Real-Time Image Processing 10(4):599-609, Springer, 2015.
Mircea Serban Pavel, Hannes Schulz and Sven Behnke:
Recurrent
Convolutional
Neural Networks for Object-Class Segmentation of RGB-D Video
In Proceedings of International Joint Conference on Neural Networks
(IJCNN), Killarney, Ireland, July 2015
Max Schwarz, Hannes Schulz, and Sven Behnke:
RGB-D Object Recognition and
Pose Estimation based on Pre-trained Convolutional Neural Network
Features
In Proceedings of IEEE International Conference on Robotics and
Automation (ICRA), Seattle, USA, May 2015.
Hannes Schulz, Nico Höft, and Sven Behnke:
Depth
and Height Aware Semantic RGB-D Perception with Convolutional Neural
Networks
In Proceedings of European Symposium on Artificial Neural Networks
(ESANN), April 2015.
Hannes Schulz, Benedikt Waldvogel, Rasha Sheikh, and Sven Behnke:
CURFIL:
Random Forests for Image Labeling on GPU
In Proceedings of 10th International Conference on Computer Vision
Theory and Applications (VISAPP), March 2015.
[Source code]
Hannes Schulz, Kyunghyun Cho, Tapani Raiko, and Sven Behnke:
Two-Layer
Contractive
Encodings for Learning Stable Nonlinear Features
Neural Networks 64:4-11, Elsevier, 2015.
Nico Höft, Hannes Schulz and Sven Behnke:
Fast Semantic Segmentation of
RGB-D Scenes with GPU-Accelerated Deep Neural Networks
In Proceedings of 37th German Conference on Artificial
Intelligence (KI), pp. 80-85, Springer LNCS 8736,
Stuttgart, September 2014.
Hannes Schulz and Sven Behnke:
Structured
Prediction
for Object Detection in Deep Neural Networks
In Proceedings of 24th International Conference on Artificial Neural
Networks (ICANN), Hamburg, September 2014.
Hannes Schulz, Kyunghyun Cho, Tapani Raiko, and Sven Behnke:
Two-Layer Contractive
Encodings with Shortcuts for Semi-supervised Learning
In Proceedings of 20th International Conference on Neural Information
Processing (ICONIP), pp. 450-457, Daegu, Korea, November 2013.
Andreas Müller and Sven Behnke:
Multi-Instance
Methods
for Partially Supervised Image Segmentation
In Proceedings of First IAPR Workshop on Partially Supervised Learning
(PSL), Ulm, September 2011.
Hannes Schulz and Sven Behnke:
Object-Class
Segmentation
using Deep Convolutional Neural Networks
DAGM Workshop on New Challenges in Neural Computation (NC2),
Frankfurt,
August
2011.
Hannes Schulz, Andreas Müller, and Sven Behnke:
Exploiting
Local
Structure in Boltzmann Machines
Neurocomputing 74(9):1411-1417, Elsevier, April 2011.
Hannes Schulz, Andreas Müller, and Sven Behnke:
Investigating
Convergence
of Restricted Boltzmann Machine Learning
In Proceedings of NIPS Workshop on Deep Learning and Unsupervised
Feature Learning, Whistler, Canada, December 2010.
Dominik Scherer, Andreas Müller, and Sven Behnke:
Evaluation
of Pooling Operations in Convolutional Architectures for Object
Recognition
In Proceedings of 20th International Conference on Artificial Neural
Networks (ICANN), Thessaloniki, Greece, September 2010.
Dominik Scherer, Hannes Schulz, and Sven Behnke:
Accelerating
Large-Scale Convolutional Neural Networks with Parallel Graphics
Multiprocessors
In Proceedings of 20th International Conference on Artificial Neural
Networks (ICANN), Thessaloniki, Greece, September 2010.
Andreas Müller, Hannes Schulz, and Sven Behnke:
Topological Features in Locally Connected RBMs
In Proceedings of International Joint Conference on Neural Networks
(IJCNN), July 2010.
Hannes Schulz, Andreas Müller, and Sven Behnke:
Exploiting local structure in stacked Boltzmann machines
In Proceedings of European Symposium on Artificial Neural Networks,
Computational Intelligence and Machine Learning (ESANN), Bruges,
Belgium, April 2010.
Dominik Scherer and Sven Behnke:
Accelerating
Large-scale Convolutional Neural Networks with Parallel Graphics
Multiprocessors
In Proceeding of NIPS Workshop on Large-Scale Machine Learning:
Parallelism and Massive Datasets, Whistler, Canada, December 2009.
Rafael Uetz and Sven Behnke:
Locally-Connected
Hierarchical
Neural Networks for GPU-accelerated Object Recognition
In Proceeding of NIPS Workshop on Large-Scale Machine Learning:
Parallelism and Massive Datasets, Whistler, Canada, December 2009.
Rafael Uetz and Sven Behnke:
Large-scale
Object Recognition with CUDA-accelerated Hierarchical Neural
Networks
In Proceedings of the 1st IEEE International Conference on Intelligent
Computing and Intelligent Systems (ICIS), Shanghai, China,
November 2009.
Sven Behnke:
Face
Localization
and Tracking in the Neural Abstraction Pyramid.
Neural Computing and Applications 14(2), pp. 97-103, Jul. 2005.
(online Nov. 2004).
Sven Behnke:
Discovering
hierarchical
speech features using convolutional non-negative matrix
factorization.
Proceedings of International Joint Conference on Neural Networks
(IJCNN), vol. 4, pp. 2758-2763, Portland, OR, July 2003.
Sven Behnke:
A
Two-Stage System for Meter Value Recognition.
In Proceedings of IEEE International Conference on Image Processing
(ICIP), vol. I, pp. 549-552, Barcelona, Spain, September 2003.
Sven Behnke:
Face
Localization
in the Neural Abstraction Pyramid.
In Proceedings of Seventh International Conference on Knowledge-Based
Intelligent Information & Engineering Systems (KES'03), Oxford,
UK, LNAI 2774, vol. 2, pp. 139-145, September 2003.
Sven Behnke:
Learning
Iterative
Binarization using Hierarchical Recurrent Networks.
In Proceedings of Joint 13th International Conference on Artificial
Neural Networks and 10th International Conference on Neural
Information Processing ICANN/ICONIP 2003, Istanbul, Turkey, pp.
306-309, June 2003.
Sven Behnke:
Meter
Value
Recognition using Locally Connected Hierarchical Networks.
In Proceedings of 11th European Symposium on Artificial Neural
Networks ESANN'03 -- Bruges, Belgium, pp. 535-540, 2003.
Sven Behnke:
Hierarchical
Neural
Networks for Image Interpretation.
Lecture Notes in Computer Science 2766, Springer, 2003.
Sven Behnke:
Learning
Face
Localization Using Hierarchical Recurrent Networks.
In Proceedings of International Conference on Artificial Neural
Networks (ICANN), Madrid, Spain, pp. 1319-1324, 2002.
Sven Behnke:
Learning
Iterative
Image Reconstruction in the Neural Abstraction Pyramid.
International Journal of Computational Intelligence and Applications,
Special Issue on Neural Networks at IJCAI-2001, vol. 1, no. 4, pp.
427-438, 2001.
Sven Behnke:
Learning
Iterative
Image Reconstruction.
Proceedings of Seventeenth International Joint Conference on
Artificial Intelligence (IJCAI), Seattle, USA, pp. 1353-1358, 2001.
Sven Behnke:
Hebbian
learning
and competition in the Neural Abstraction Pyramid
In Proceedings of International Joint Conference on Neural Networks
(IJCNN'99) -- Washington, DC, paper number #491, 1999.
Sven Behnke and Raul Rojas:
Activity
Driven Update in the Neural Abstraction Pyramid
Proceedings of the 8th International Conference on Artificial Neural
Networks (ICANN), Skövde, Sweden, pp 567-572, September 1998.
Sven Behnke and Raul Rojas:
Neural
Abstraction
Pyramid: A hierarchical image understanding architecture
In Proceedings of International Joint Conference on Neural Networks
(IJCNN'98) -- Anchorage, AL, vol. 2, pp. 820-825, 1998.
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