2021

• J. Tu, H. Li, X. Yan, M. Ren, Y. Chen, M. Liang, E. Bitar, E. Yumer and R. Urtasun
  Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving [pdf] 
  In Conference on Robot Learning (CoRL), London UK, November 2021


• S. Segal, N. Kumar, S. Casas, W. Zeng, M. Ren, J. Wang, and R. Urtasun
  Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes [pdf] 
  In Conference on Robot Learning (CoRL), London UK, November 2021


• A. Cui, A. Sadat, S. Casas, R. Liao, R. Urtasun
  LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving [pdf]  (oral)
  In International Conference on Computer Vision (ICCV), October 2021


• J. Tu, T. Wang, J. Wang, S. Manivasagam, M. Ren and R. Urtasun
  Adversarial Attacks On Multi-Agent Communication [pdf] 
  In International Conference on Computer Vision (ICCV), October 2021


• Y. Xiong, M. Ren, W. Zeng, R. Urtasun
  Self-Supervised Representation Learning from Flow Equivariance [pdf] 
  In International Conference on Computer Vision (ICCV), October 2021


• K. Luo, S. Casas, R. Liao, X. Yan, Y. Xiong, W. Zeng and R. Urtasun
  Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting [pdf] 
  In International Conference on Intelligent Robots and Systems (IROS), Prague, Check Republic, October 2021


• Y. Wang, B. Yang, R. Hu, M. Liang, and R. Urtasun
  PLUME: Efficient 3D Object Detection from Stereo Images [pdf] 
  In International Conference on Intelligent Robots and Systems (IROS), Prague, Check Republic, October 2021


• A. Sadat, S. Segal, S. Casas, J. Tu, B. Yang, R. Urtasun and E. Yumer
  Diverse Complexity Measures for Dataset Curation in Self-driving [pdf] 
  In International Conference on Intelligent Robots and Systems (IROS), Prague, Check Republic, October 2021


• Y. Chen, F. Rong, S. Duggal, S. Wang, X. Yan, S. Manivasagam, S. Xue, E. Yumer R. Urtasun
  GeoSim: Photorealistic Image Simulation with Geometry-Aware Composition [pdf]  (Finalist Best Paper Award)
  In Conference on Computer Vision and Pattern Recognition (CVPR), Virtual, June 2021


• S. Casas, A. Sadat and R. Urtasun
  MP3: A Unified Model to Map, Perceive, Predict and Plan [pdf]  (Finalist Best Paper Award)
  In Conference on Computer Vision and Pattern Recognition (CVPR), Virtual, June 2021


• J. Martinez, J. Shewakramani, T. Wei Liu, A. Barsan, W. Zeng, and R. Urtasun
  Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks [pdf]  (oral)
  In Conference on Computer Vision and Pattern Recognition (CVPR), Virtual, June 2021


• J. Wang, A. Pun, J. Tu, S. Manivasagam, A. Sadat, S. Casas, M. Ren, R. Urtasun
  AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles [pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Virtual, June 2021


• Z. Yang, S. Wang, S. Manivasagam, Z. Huang, W-C. Ma, X. Yan, E. Yumer and R. Urtasun
  S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling [pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Virtual, June 2021


• S. Suo, S. Regalado, S. Casas and R. Urtasun
  TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors [pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Virtual, June 2021


• S. Tan, K. Wong, S. Wang, S. Manivasagam, M. Ren and R. Urtasun
  SceneGen: Learning to Generate Realistic Traffic Scenes [pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Virtual, June 2021


• J. Phillips, J. Martinez, I. A. Bârsan, S. Casas, A. Sadat and R. Urtasun
  Deep Multi-Task Learning for Joint Localization, Perception, and Prediction [pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Virtual, June 2021


• B. Wei*, M. Ren*, W. Zeng, M. Liang, B. Yang and R. Urtasun
  Perceive, attend, and drive: Learning spatial attention for safe self-driving [pdf] 
  In International Conference on Robotics and Automation (ICRA), Xian, China, May 2021


• A. J. Yang, C. Cui, I. A. Barsan, R. Urtasun and S. Wang
  Asynchronous Multi-View SLAM [pdf] 
  In International Conference on Robotics and Automation (ICRA), Xian, China, May 2021


• J. Liu, W. Zeng, R. Urtasun and E. Yumer
  Deep Structured Reactive Planning [pdf] 
  In International Conference on Robotics and Automation (ICRA), Xian, China, May 2021


• R. Liao, R. Urtasun and R. Zemel
  PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks [pdf] 
  In International Conference on Learning Representations (ICLR), Vienna, Austria, May 2021


• X. Zeng, R. Urtasun, R. Zemel, S. Fidler and R. Liao
  NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image Generation[pdf] 
  In Arxiv preprint, arXiv:2106.13435, June 2021


• B. Yang, M. Bai, M. Liang, W. Zeng and R. Urtasun
  Auto4D: Learning to Label 4D Objects from Sequential Point Clouds [pdf] 
  In Arxiv preprint, arXiv:2101.06586, Jan 2021


• N. Homayounfar, J. Liang, W.-C. Ma R. Urtasun
  VideoClick: Video Object Segmentation with a Single Click [pdf] 
  In Arxiv preprint, arXiv:2101.06545, Jan 2021


• M. Bai, S. Wang, K. Wong, E. Yumer and R. Urtasun
  Non-parametric Memory for Spatio-Temporal Segmentation of Construction Zones for Self-Driving [pdf] 
  In Arxiv preprint, arXiv:2101.06865, Jan 2021


• S. Duggal, Z. Wang, W-C Ma, S. Manivasagam, J. Liang, S. Wang and R. Urtasun
  Secrets of 3D Implicit Object Shape Reconstruction in the Wild [pdf] 
  In Arxiv preprint, arXiv:2101.06860, Jan 2021


• W. Zeng, Y. Xiong and R. Urtasun
  Network Automatic Pruning: Start NAP and Take a Nap [pdf] 
  In Arxiv preprint, arXiv:2101.06608, Jan 2021


• J. Wang, M. Ren, I. Bogunovic, Y. Xiong and R. Urtasun
  Cost-Efficient Online Hyperparameter Optimization [pdf] 
  In Arxiv preprint, arXiv:2101.06590, Jan 2021

2020

• D. Frossard, S. Suo, S. Casas, J. Tu and R. Urtasun
  StrObe: Streaming Object Detection from LiDAR Packets [pdf] 
  In Conference on Robot Learning (CoRL), Cambridge, Massachusetts, US, November 2020


• S. Segal, E. Kee, W. Luo, A. Sadat, E. Yumer and R. Urtasun
  Universal Embeddings for Spatio-Temporal Tagging of Self-Driving Logs [pdf]  (oral)
  
  In Conference on Robot Learning (CoRL), Cambridge, Massachusetts, US, November 2020


• Z. Yang, S. Manivasagam, M. Liang, B. Yang, W-C. Ma and R. Urtasun
  Recovering and Simulating Pedestrians in the Wild [pdf] 
  In Conference on Robot Learning (CoRL), Cambridge, Massachusetts, US, November 2020


• M. Shah, Z. Huang, A. Laddha, M. Langford, B. Barber, S. Zhang, C. Vallespi-Gonzalez and R. Urtasun
  LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusion [pdf] 
  In Conference on Robot Learning (CoRL), Cambridge, Massachusetts, US, November 2020


• N. Vadivelu, M. Ren, J. Tu, J. Wang and R. Urtasun
  Learning to Communicate and Correct Pose Errors [pdf] 
  In Conference on Robot Learning (CoRL), Cambridge, Massachusetts, US, November 2020


• Y. Xiong, M. Ren and R. Urtasun
  LoCo: Local Contrastive Representation Learning [pdf] 
  In Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 2020


• S. Biswas, J. Liu, K. Wong, S. Wang and R. Urtasun
  MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models [pdf] 
  In Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 2020


• J. Martinez, S. Doubov, J. Fan, I. A. Barsan, S. Wang, G. Mattyus and R. Urtasun
  Pit30M: A Benchmark for Global Localization in the Age of Self-Driving Cars [pdf coming soon]  (Finalist Best Applicaton Paper Award)
  In International Conference on Intelligent Robots and Systems (IROS), Las Vegas, Nevada, US, November 2019


• S. Casas, C. Gulino, S. Su and R. Urtasun
  The Importance of Prior Knowledge in Precise Multimodal Prediction [pdf]  (oral)
  In International Conference on Intelligent Robots and Systems (IROS), Las Vegas, Nevada, US, November 2019


• L. Li, B. Yang, M. Liang, W. Zeng, M. Ren, S. Segal and R. Urtasun
  End-to-end Contextual Perception and Prediction with Interaction Transformer [pdf]  (oral)
  In International Conference on Intelligent Robots and Systems (IROS), Las Vegas, Nevada, US, November 2019


• X. Qi, Z. Liu, R. Liao, P. H. S. Torr, R. Urtasun and J. Jia
  GeoNet++: Iterative Geometric Neural Network with Edge-Aware Refinement for Joint Depth and Surface Normal Estimation [pdf] 
  In Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2020


• T-H. Wang, S. Manivasagam, M. Liang, B. Yang, W. Zeng and R. Urtasun
  V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction [pdf]  (oral)
  In European Conference in Computer Vision (ECCV), Glasgow, Scottland, August 2020


• M. Liang, B. Yang, R. Hu, Y. Chen, R. Liao, S. Feng and R. Urtasun
  Learning Lane Graph Representations for Motion Forecasting [pdf]  (oral)
  In European Conference in Computer Vision (ECCV), Glasgow, Scottland, August 2020


• W-C. Ma, S. Wang, J. Gu, S. Manivasagam, A. Torralba and R. Urtasun
  Deep Feedback Inverse Problem Solver [pdf]  (spotlight)
  In European Conference in Computer Vision (ECCV), Glasgow, Scottland, August 2020


• J. Gu, W-C. Ma, S. Manivasagam, W. Zeng, Z. Wang, Y. Xiong, Hao Su and R. Urtasun
  Weakly-supervised 3D Shape Completion in the Wild [pdf]  (spotlight)
  In European Conference in Computer Vision (ECCV), Glasgow, Scottland, August 2020


• A. Sadat*, S. Casas*, M. Ren, X. Wu, P, Dhawan and R. Urtasun
  Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations [pdf] 
  In European Conference in Computer Vision (ECCV), Glasgow, Scottland, August 2020


• W. Zeng, S. Wang, R. Liao, Y. Chen, B. Yang R. Urtasun
  DSDNet: Deep Structured self-Driving Network [pdf] 
  In European Conference in Computer Vision (ECCV), Glasgow, Scottland, August 2020


• S. Casas*, C. Gulino*, S. Suo*, K. Luo, R. Liao and R. Urtasun
  Implicit Latent Variable Model for Scene-Consistent Motion Forecasting [pdf] 
  In European Conference in Computer Vision (ECCV), Glasgow, Scottland, August 2020


• K. Wong, Q. Zhang, M. Liang, B. Yang, R. Liao, A. Sadat and R. Urtasun
  Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction [pdf] 
  In European Conference in Computer Vision (ECCV), Glasgow, Scottland, August 2020


• N. Homayounfar, Y. Xiong, J. Liang, W-C. Ma and R. Urtasun
  LevelSet R-CNN: A Deep Variational Method for Instance Segmentation [pdf] 
  In European Conference in Computer Vision (ECCV), Glasgow, Scottland, August 2020


• B. Yang*, R. Guo*, M. Liang, S. Casasn and R. Urtasun
  RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects [pdf] 
  In European Conference in Computer Vision (ECCV), Glasgow, Scottland, August 2020


• J. Liu, S. Wang, W-C. Ma, M. Shah, R. Hu, P. Dhawan and R. Urtasun
  Conditional Entropy Coding for Efficient Video Compression [pdf coming soon] 
  In European Conference in Computer Vision (ECCV), Glasgow, Scottland, August 2020


• Z. Yang, Y. Xu, H. Xue, Z. Zhang, R. Urtasun, Liwei Wang, Stephen Lin and Han Hu
  Dense RepPoints: Representing Visual Objects with Dense Point Sets [pdf] 
  In European Conference in Computer Vision (ECCV), Glasgow, Scottland, August 2020


• Q. Sykora, M. Ren and R. Urtasun
  Multi-Agent Routing Value Iteration Network [pdf]  (oral)
  In International Conference in Machine Learning (ICML), Viena, Austria, July 2020


• C. H. Lim R. Urtasun and E. Yumer
  Hierarchical Verification for Adversarial Robustness [pdf]  (oral)
  In International Conference in Machine Learning (ICML), Viena, Austria, July 2020


• S. Manivasagam, S. Wang, K. Wong, W. Zeng, B. Yang, S. Tan, M. Sazanovich, W.C. Ma and R. Urtasun
  LidarSIM: Realistic LiDAR Simulation by Leveraging the Real World [pdf]  (oral)
  In Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, June 2020


• L. Huang, S. Wang, K. Wong, J. Liu and R. Urtasun
  OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression [pdf]  (oral)
  In Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, June 2020


• J. Tu, M. Ren, S. Manivasagam, M. Liang, B. Yang, R. Du, F. Cheng and R. Urtasun
  Physically Realizable Adversarial Examples for LiDAR Object Detection [pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, June 2020


• M. Liang*, B. Yang*, W. Zeng, Y. Chen, R. Hu, S. Casas and R. Urtasun
  PnPNet: End-to-End Perception and Prediction with Tracking in the Loop [pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, June 2020


• J. Liang, N. Homayounfar, W.C. Ma, Y. Xiong, R. Hu and R. Urtasun
  PolyTransform: Deep Polygon Transformer for Instance Segmentation [pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, June 2020


• S. Casas, C. Gulino, R. Liao and R. Urtasun
  Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data [pdf]  (oral)
  In International Conference on Robotics and Automation (ICRA), Paris, France, May 2020

2019

• R. Liao, Y. Li, Y. Song, S. Wang, W. Hamilton, D. Duvenaud, R. Urtasun and R. Zemel
  Efficient Graph Generation with Graph Recurrent Attention Networks [pdf] 
  In Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 2019


• K. Wong, S. Wang, M. Ren, M. Liang and R. Urtasun
  Identifying Unknown Instances for Autonomous Driving [pdf]  (spotlight)
  In Conference on Robot Learning (CoRL), Osaka, Japan, November 2019


• A. Jain, S. Casas, R. Liao, Y. Xiong, S. Feng, S. Segal and R. Urtasun
  Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction [pdf]  (spotlight)
  In Conference on Robot Learning (CoRL), Osaka, Japan, November 2019


• Y. Xiong*, M. Ren*, R. Liao, K. Wong and R. Urtasun
  Deformable Filter Convolution for Point Cloud Reasoning [pdf] 
  In Arxiv preprint, arXiv:1907.13079, July 2019


• A. Sadat*, M. Ren*, A. Pokrovsky, Y. C. Lin, E. Yumer and R. Urtasun
  Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles [pdf]  (oral)
  In International Conference on Intelligent Robots and Systems (IROS), Macau, China, November 2019


• W.C Ma*, I. Tartavull*, I. A. Barsan*, S. Wang*, M. Bai, G. Mattyus, N. Homayounfar, S. K. Lakshmikanth, A. Pokrovsky and R. Urtasun
  Exploiting Sparse Semantic HD Maps for Affordable Localization [pdf]  (oral)
  In International Conference on Intelligent Robots and Systems (IROS), Macau, China, November 2019


• J. Liu, S. Wang and R. Urtasun
  Deep Stereo Image Compression [pdf]  (oral)
  In International Conference on Computer Vision (ICCV), Seoul, Korea, October 2019


• N. Homayounfar*, J. Liang*, W. C. Ma, J. Fan, X. Wu and R. Urtasun
  DAGMapper: Learning to Map by Discovering Lane Topology [pdf] 
  In International Conference on Computer Vision (ICCV), Seoul, Korea, October 2019


• S. Duggal, S. Wang, W. C. Ma, R. Hu and R. Urtasun
  Differentiable Deep PatchMatch for Efficient Stereo Matching [pdf] 
  In International Conference on Computer Vision (ICCV), Seoul, Korea, October 2019


• Y. Chen, M. Liang, B. Yang and R. Urtasun
  Learning Joint 2D-3D Representations for Depth Completion [pdf] 
  In International Conference on Computer Vision (ICCV), Seoul, Korea, October 2019


• X. Zeng, R. Liao, L. Gu, Y. Xiong, S. Fidler R. Urtasun
  DMM-Net: Differentiable Mask-Matching Network for Video Instance Segmentation [pdf] 
  In International Conference on Computer Vision (ICCV), Seoul, Korea, October 2019


• W. Zeng*, W. Luo*, S. Suo, A. Saddat, B. Yang, S. Casas and R. Urtasun
  End-to-end Interpretable Neural Motion Planner [pdf]  (oral)
  In Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, California, June 2019


• Y. Xiong*, R. Liao*, H. Zhang*, R. Hui, E. Yumer and R. Urtasun
  UPSNet: A Unified Panoptic Segmentation Network [pdf]  (oral)
  In Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, California, June 2019


• W.C. Ma, S. Wang, R. Hu, Y. Xiong and R. Urtasun
  Deep Structured Scene Flow [pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, California, June 2019


• D. Chen, R. Liao, S. Fidler and R. Urtasun
  DARNet: Deep Active Ray Network for Building Segmentation [pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, California, June 2019


• M Liang*, B. Yang*, Y. Chen, R. Hui and R. Urtasun
  Multi-Task Multi-Sensor Fusion for 3D Object Detection [pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, California, June 2019


• X. Wei*, S. Wang*, J. Martinez, A. Barsan and R. Urtasun
  Learning to Localize through Compressed Binary Maps [pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, California, June 2019


• J. Liang*, N. Homayounfar*, S. Wang, W. C. Ma and R. Urtasun
  Convolutional Recurrent Network for Road Boundary Extraction [pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, California, June 2019


• D. Frossard, E. Kee and R. Urtasun
  DeepSignals: Predicting Intent of Drivers Through Visual Attributes [pdf] 
  In International Conference on Robotics and Automation (ICRA), Montreal, Canada, May 2019


• R. Liao, Z. Zhao, R. Urtasun and R. Zemel
  LanczosNet: Multi-Scale Deep Graph Convolutional Networks [pdf] 
  In International Conference on Learning Representations, (ICLR), New Orleans, May 2019


• C. Zhang, M. Ren and R. Urtasun
  Graph HyperNetworks for Neural Architecture Search [pdf] 
  In International Conference on Learning Representations, (ICLR), New Orleans, May 2019


• M. T Law, J. Snell, A.Farahmand, R. Urtasun and R. Zemel
  Dimensionality Reduction for Representing the Knowledge of Probabilistic Models [pdf] 
  In International Conference on Learning Representations, (ICLR), LNew Orleans, May 2019

2018

• L. Zhang, G. Rosenblatt, E. Fetaya, R. Liao, W. E Byrd, R. Urtasun and R. Zemel
  Neural Guided Constraint Logic Programming for Program Synthesis [pdf] 
  In Neural Information Processing Systems (NeurIPS), Montreal, Canada, December 2018


• S. Casas, W. Luo and R. Urtasun
  IntentNet: Learning to Predict Intention from Raw Sensor Data [pdf] 
  In Conference on Robot Learning (CoRL), Zurich, Switzerland, October 2018


• A. Barsan, S. Wang, A. Pokrovsky and R. Urtasun
  Learning to Localize Using a LiDAR Intensity Map [pdf] 
  In Conference on Robot Learning (CoRL), Zurich, Switzerland, October 2018


• B. Yang, M. Liang and R. Urtasun
  HDNET: Exploiting HD Maps for 3D Object Detection [pdf] 
  In Conference on Robot Learning (CoRL), Zurich, Switzerland, October 2018


• M. Bai, G. Mattyus, N. Homayounfar, S. Wang, S. Kowshika Lakshmikanth and R. Urtasun
  Deep Multi-Sensor Lane Detection [pdf] 
  In International Conference on Intelligent Robots (IROS), Madrid, Spain, October 2018


• M. Liang, B. Yang, S. Wang and R. Urtasun
  Deep Continuous Fusion for Multi-Sensor 3D Object Detection [pdf] 
  In European Conference in Computer Vision (ECCV), Munich, Germanh, September 2018


• J. Liang and R. Urtasun
  End-to-End Deep Structured Models for Drawing Crosswalks [pdf] 
  In European Conference in Computer Vision (ECCV), Munich, Germanh, September 2018


• W. Ma, H. Chu, B. Zhou, R. Urtasun and A. Torralba
  Single Image Intrinsic Decomposition Without a Single Intrinsic Image [pdf] 
  In European Conference in Computer Vision (ECCV), Munich, Germanh, September 2018


• C. Zhang, W. Luo and R. Urtasun
  Efficient Convolutions for Real-Time Semantic Segmentation of 3D Point Clouds [pdf] 
  In International Conference on 3D Vision (3DV), Verona, Italy, September 2018


• M. Teichmann, M. Weber, M. Zollner, R. Cipolla and R. Urtasun
  MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving [pdf]  (best poster award)
  In Intelligent Vehicle Symposium (IV), Changshu, China, June 2018


• R. Liao, Y. Xiong, E. Fetaya, L. Zhang, K. Yoon, X. Pitkow, R. Urtasun and R. Zemel
  Reviving and Improving Recurrent Back-Propagation [pdf]  (oral)
  In International Conference in Machine Learning (ICML), Stockholm, Sweeden, July 2018


• M. Ren, W. Zeng, B. Yang and R. Urtasun
  Learning to Reweight Examples for Robust Deep Learning [pdf]  (oral)
  In International Conference in Machine Learning (ICML), Stockholm, Sweeden, July 2018


• W. Luo, B. Yang and R. Urtasun
  Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net [pdf]  (oral)
  In Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah, US, June 2018


• S. Wang, S. Suo, W.C. Ma, A. Pokrovsky and R. Urtasun
  Deep Parametric Continuous Convolutional Neural Networks [pdf]  (spotlight)
  In Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah, US, June 2018


• M Ren, A. Pokrovsky, B. Yang and R. Urtasun
  SBNet: Sparse Blocks Network for Fast Inference [pdf]  (spotlight)
  In Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah, US, June 2018


• D. Marcos, D. Tuia, B. Kellenberger, L. Zhang, M. Bai, R. Liao and R. Urtasun
  Learning deep structured active contours end-to-end [pdf]  (spotlight)
  In Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah, US, June 2018


• B. Yang, W. Luo and R. Urtasun
  PIXOR: Real-time 3D Object Detection from Point Clouds [pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah, US, June 2018


• N. Homayounfar, W. C. Ma, S. K. Lakshmikanth and R. Urtasun
  Hierarchical Recurrent Attention Networks for Structured Online Maps [pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah, US, June 2018


• G. Mattyus and R. Urtasun
  Matching Adversarial Networks [pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah, US, June 2018


• X. Qi, R. Liao, Z. Liu, R. Urtasun and J. Jia
  GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation[pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah, US, June 2018


• H. Chu, W.C. Ma, K. Kundu, R. Urtasun and S. Fidler
  SurfConv: Bridging 3D and 2D Convolution for RGBD Images[pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah, US, June 2018


• D. Frossard and R. Urtasun
  End-To-End Learning of Multi-Sensor 3D Tracking by Detection[pdf] 
  In International Conference on Robotics and Automation (ICRA), Brisbane, Australia, May 2018

2017

• A. Gomez, M. Ren, R. Urtasun and R. Grosse
  The Reversible Residual Network: Backpropagation Without Storing Activations [pdf] 
  In Neural Information Processing Systems (NIPS), Long Beach, California, December 2017


• E. Triantafillou, R. Zemel and R. Urtasun
  Few-Shot Learning Through an Information Retrieval Lens [pdf] 
  In Neural Information Processing Systems (NIPS), Long Beach, California, December 2017


• X. Qi, R. Liao, J. Ya, S. Fidler and R. Urtasun
  3D Graph Neural Networks for RGBD Semantic Segmentation [pdf]  (oral)
  In International Conference on Computer Vision (ICCV), Venice, Italy, October 2017


• B. Dai, D. Lin, R. Urtasun and S. Fidler
  Towards Diverse and Natural Image Descriptions via a Conditional GAN [pdf]  (oral)
  In International Conference on Computer Vision (ICCV), Venice, Italy, October 2017


• S. Wang, M. Bai, G. Mattyus, H. Chu, W. Luo, B. Yang, J. Liang, J. Cheverie, S. Fidler and R. Urtasun
  TorontoCity: Seeing the World with a Million Eyes [pdf]  (spotlight)
  In International Conference on Computer Vision (ICCV), Venice, Italy, October 2017


• S. Liu, J. Ya, S. Fidler and R. Urtasun
  Sequential Grouping Networks for Instance Segmentation [pdf] 
  In International Conference on Computer Vision (ICCV), Venice, Italy, October 2017


• S. Zhu, C. Loy, D. Ling, R. Urtasun and S. Fidler
  Be Your Own Prada: Fashion Synthesis with Structural Coherence [pdf] 
  In International Conference on Computer Vision (ICCV), Venice, Italy, October 2017


• R. Li, M. Tapaswi, R. Liao, J. Jia, R. Urtasun and S. Fidler
  Situation Recognition with Graph Neural Networks [pdf] 
  In International Conference on Computer Vision (ICCV), Venice, Italy, October 2017


• G. Mattyus, W. Luo and R. Urtasun
  DeepRoadMapper: Extracting Road Topology from Aerial Images [pdf] 
  In International Conference on Computer Vision (ICCV), Venice, Italy, October 2017


• N. Merkle, W. Luo, S. Auer, R. Muller and R. Urtasun
  Exploiting Deep Matching and SAR Data for the Geo-Localization Accuracy Improvement of Optical Satellite Images [pdf] 
  In Remote Sensing 2017


• M. Law, R. Urtasun and R. Zemel
  Deep Spectral Clustering Learning [pdf]   (oral)
  In Internatinal Conference in Machine Learning (ICML), Sydney, Australia, August 2017


• L. Castrejon, K. Kundu, R. Urtasun and S. Fidler
  Annotating Object Instances with a Polygon-RNN [pdf]  (oral, best paper runner up award)
  In Conference on Computer Vision and Pattern Recognition (CVPR), Hawaii, US, July 2017


• M. Bai and R. Urtasun
  Deep Watershed Transform for Instance Segmentation [pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Hawaii, US, July 2017


• N. Homayounfar, S. Fidler and R. Urtasun
  Sports Field Localization via Deep Structured Models [pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Hawaii, US, July 2017


• M. Law, Y. Yu, R. Urtasun, R. Zemel and E. Xing
  Efficient Multiple Instance Metric Learning using Weakly Supervised Data [pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Hawaii, US, July 2017


• X. Chen, K. Kundu, Y. Zhu, H. Ma, S. Fidler and R. Urtasun
  3D Object Proposals using Stereo Imagery for Accurate Object Class Detection [pdf] 
  In Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2017


• M. Ren, R. Liao, R. Urtasun, F. H. Sinz and R. Zemel
  Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes [pdf] 
  In International Conference on Learning Representations, (ICLR), Toulon, France, May 2017


• W. Ma, S. Wang, M. A. Brubaker, S. Fidler and R. Urtasun
  Find Your Way by Observing the Sun and Other Semantic Cues [pdf]
  In International Conference on Robotics and Automation (ICRA), Singapore, May 2017


• W. Zeng, W. Luo, S. Fidler and R. Urtasun
  Efficient Summarization with Read-Again and Copy Mechanism [pdf] 
  In Arxiv preprint, arXiv:1611.03382, Nov 2016


• H. Chu, R. Urtasun and S. Fidler
  Song From PI: A Musically Plausible Network for Pop Music Generation [pdf] 
  In Arxiv preprint, arXiv:1611.03477, Nov 2016

2016

• S. Wang, S. Fidler and R. Urtasun
  Proximal Deep Structured Models [pdf]
  In Neural Information Processing Systems (NIPS), Barcelona, Spain, December 2016


• R. Liao, A. Schwing, R. Zemel and R. Urtasun
  Learning Deep Parsimonious Representations [pdf]
  In Neural Information Processing Systems (NIPS), Barcelona, Spain, December 2016


• W. Luo, Y. Li, R. Urtasun and R. Zemel
  Understanding the Effective Receptive Field in Deep Convolutional Neural Networks [pdf]
  In Neural Information Processing Systems (NIPS), Barcelona, Spain, December 2016


• M. Bai, W. Luo, K. Kundu and R. Urtasun
  Exploiting Semantic Information and Deep Matching for Optical Flow [pdf]  
  In European Conference in Computer Vision (ECCV), Amsterdam, Netherlands, October 2016


• H. Chu, S. Wang, R. Urtasun and S. Fidler
  HouseCraft: Building Houses from Rental Ads and Street Views [pdf]  
  In European Conference in Computer Vision (ECCV), Amsterdam, Netherlands, October 2016


• T. Hazan, A. Schwing and R. Urtasun
  Blending Learning and Inference in Conditional Random Fields [pdf]  
  In Journal of Machine Learning Research (JMLR), 2016


• Y. Song, A. Schwing, R. Zemel and R. Urtasun
  Training Deep Neural Networks via Direct Loss Minimization [pdf]   (oral)
  In Internatinal Conference in Machine Learning (ICML), New York, US, June 2016


• W. Luo, A. Schwing and R. Urtasun
  Efficient Deep Learning for Stereo Matching [pdf] [project/Code]  (spotlight)
  In Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, US, June 2016


• M. Tapaswi, Y. Zhu, R. Stiefelhagen, R. Urtasun and S. Fidler
  MovieQA: Understanding Stories in Movies through Question-Answering [pdf][Benchmark]  (spotlight)
  In Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, US, June 2016


• G. Mattyus, S. Wang, S. Fidler and R. Urtasun
  HD Maps: Fine-grained Road Segmentation by Parsing Ground and Aerial Images [pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, US, June 2016


• X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun
  Monocular 3D Object Detection for Autonomous Driving [pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, US, June 2016


• Z. Zhang, S. Fidler and R. Urtasun
  Instance-Level Segmentation with Deep Densely Connected MRFs [pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, US, June 2016


• I. Vendov, R. Kiros, S. Fidler and R. Urtasun
  Order-Embeddings of Images and Language [pdf]  (oral)
  In International Conference on Learning Representations, (ICLR), San Juan, Puerto Rico, May 2016


• Y. Wang, M. Brubaker and R. Urtasun
  Sequential Inference for Deep Gaussian Process [pdf] 
  In International Conference on Artificial Intelligence and Statistics (AISTATS), Cadiz, Spain, May 2016


• M. Brubaker, A. Geiger and R. Urtasun
  Map-Based Probabilistic Visual Self-Localization [pdf] 
  In Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2016


• R. Mottaghi, S. Fidler, A. Yuille, R. Urtasun and Devi Parikh
  Human-Machine CRFs for Identifying Bottlenecks in Scene Understanding [pdf] 
  In Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2016

2015

• P. Judd, J. Albericio, T. Hetherington, T. Aamodt, N. Enright Jerger R. Urtasun and A. Moshovos
  Reduced-Precision Strategies for Bounded Memory in Deep Neural Nets [pdf] 
  In Arxiv preprint, arXiv:1511.05236, Nov 2015


• X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun
  3D Object Proposals for Accurate Object Class Detection [pdf] 
  In Neural Information Processing Systems (NIPS), Montreal, Canada, December 2015


• R. Kiros, Y. Zhu, R. Salakhutdinov, R. Zemel, A. Torralba, R. Urtasun and S. Fidler
  Skip-Thought Vectors [pdf][project/Code] 
  In Neural Information Processing Systems (NIPS), Montreal, Canada, December 2015


• S. Wang, S. Fidler and R. Urtasun
  Lost Shopping! Monocular Localization in Large Indoor Spaces [pdf]  (oral)
  In International Conference on Computer Vision (ICCV), Santiago, Chile, December 2015


• Y. Zhu, R. Kiros, R. Zemel, R. Salakhutdinov, R. Urtasun, A. Torralba and S. Fidler
  Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books [pdf][project page]  (oral)
  In International Conference on Computer Vision (ICCV), Santiago, Chile, December 2015


• G. Mattyus, S. Wang, S. Fidler and R. Urtasun
  Enhancing World Maps by Parsing Aerial Images [pdf] 
  In International Conference on Computer Vision (ICCV), Santiago, Chile, December 2015


• P. Lenz, A. Geiger and R. Urtasun
  FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation [pdf] 
  In International Conference on Computer Vision (ICCV), Santiago, Chile, December 2015


• Z. Zhang, A. Schwing, S. Fidler and R. Urtasun
  Monocular Object Instance Segmentation and Depth Ordering with CNNs [pdf] 
  In International Conference on Computer Vision (ICCV), Santiago, Chile, December 2015


• A. Schwing, T. Hazan, M. Pollefeys and R. Urtasun
  Distributed Algorithms for Large Scale Learning and Inference in Graphical Models [pdf] 
  In Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2015


• A. Schwing and R. Urtasun
  Fully Connected Deep Structured Networks [pdf] 
  In arXiv:1503.02351, March 2015


• D. Lin, C. Kong, S. Fidler and R. Urtasun
  Generating Multi-Sentence Lingual Descriptions of Indoor Scenes [pdf]  (oral)
  In British Machine Vision Conference (BMVC), Swansea, Wales, September 2015


• L. C. Chen, A. Schwing, A. Yuille and R. Urtasun
  Learning Deep Structured Models [pdf]  (oral)
  In International Conference on Machine Learning (ICML), Lille, France, July 2015


• S. Wang, S.Fidler and R. Urtasun
  Holistic 3D Scene Understanding from a Single Geo-tagged Image [pdf]  (oral)
  In Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, June 2015


• C. Liu, A. Schwing, K. Kundu, R. Urtasun and S.Fidler
  Rent3D: Floor-Plan Priors for Monocular Layout Estimation [pdf]  (oral)
  In Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, June 2015


• Y. Zhu, R. Urtasun, R. Salakhutdinov and S.Fidler
  segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection [pdf]
  In Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, June 2015


• E. Simo, S. Fidler, F. Moreno-Noguer and R. Urtasun
  Neuroaesthetics in Fashion: Modeling the Perception of Beauty [pdf]
  In Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, June 2015


• J. Xu, A. Schwing and R. Urtasun
  Learning to Segment Under Various Weak Supervisions [pdf]
  In Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, June 2015


• J. Yao, M. Boben, S. Fidler and R. Urtasun
  Real-Time Coarse-to-fine Topologically Preserving Segmentation [pdf]
  In Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, June 2015


• J. Yao, S. Ramalingam, Y. Taguchi, Y. Miki and R. Urtasun
  Estimating Drivable Collision-Free Space from Monocular Video [pdf]
  In Winter Conference on Applications of Computer Vision (WACV), Hawaii, USA, January 2015

2014

• S. Wang, A. Schwing and R. Urtasun
  Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials [pdf]
  In Neural Information Processing Systems (NIPS), Montreal, Canada, December 2014


• J. Zhang, A. Schwing and R. Urtasun
  Message Passing Inference for Large Scale Graphical Models with High Order Potentials [pdf]
  In Neural Information Processing Systems (NIPS), Montreal, Canada, December 2014


• E. Simo, S. Fidler, F. Moreno-Noguer and R. Urtasun
  A High Performance CRF Model for Clothes Parsing [pdf] 
  In Asian Conference on Computer Vision (ACCV), Singapore, November 2014


• K. Yamaguchi, D. McAllester and R. Urtasun
  Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation [pdf][Project/Code] 
  In European Conference on Computer Vision (ECCV), Zurich, Switzerland, September 2014


• Y. Wang, M Brubaker, B. Chaib-draa and R. Urtasun
  Bayesian Filtering with Online Gaussian Process Latent Variable Models [pdf] 
  In Conference on Uncertainty in Artificial Intelligence (UAI), Quebec City, Canada, July 2014


• A. Schwing, T. Hazan, M. Pollefeys and R. Urtasun
  Globally Convergent Parallel MAP LP Relaxation Solver using the Frank-Wolfe Algorithm [pdf] 
  In International Conference on Machine Learning (ICML), Beijing, China, June 2014


• C. Kong, D. Lin, M. Bansal, R. Urtasun and S. Fidler
  What are you talking about? Text-to-Image Coreference [pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, USA, June 2014


• D. Lin, C. Kong, S. Fidler and R. Urtasun
  Visual Semantic Search: Retrieving Videos via Complex Textual Queries [pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, USA, June 2014


• L-C Chen, S. Fidler, A. Yuille and R. Urtasun
  Beat the MTurkers: Automatic Image Labeling from Weak 3D Supervision [pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, USA, June 2014


• J. Xu, A. Schwing and R. Urtasun
  Tell Me What You See and I will Show You Where It Is [pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, USA, June 2014


• R. Mottaghi, X Chen, X Liu, N. Cho, S. Lee, S. Fidler, R. Urtasun and A. Yuille
  The Role of Context for Object Detection and Semantic Segmentation in the Wild [pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, USA, June 2014


• X. Chen, R. Mottaghi, X. Liu, S. Fidler, R. Urtasun and A. Yuille
  Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts[pdf] 
  In Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, USA, June 2014


• S. Wang, L. Zhang and R. Urtasun
  Transductive Gaussian Processes for Image Denoising[pdf]  (oral)
  In International Conference on Computational Photography (ICCP), Santa Clara, California, May 2014

2013

• A. Geiger, M. Lauer, C. Wojek, C. Stiller and R. Urtasun.
  3D Traffic Scene Understanding from Movable Platforms
  In Pattern Analysis and Machine Intelligence (PAMI) 2013


• A. Geiger, P. Lenz, C. Stiller and R. Urtasun.
  Vision meets Robotics: The KITTI Dataset
  In International Journal of Robotics Research, (IJRR) 2013


• W. Luo, A. Schwing and R. Urtasun
  Latent Structured Active Learning [pdf]
  In Neural Information Processing Systems (NIPS), Lake Tahoe, USA, December 2013


• D. Lin, S. Fidler and R. Urtasun
  Holistic Scene Understanding for 3D Object Detection with RGBD cameras [pdf]   (oral)
  In International Conference on Computer Vision (ICCV), Sydney, Australia, December 2013


• A. Schwing, S. Fidler, M. Pollefeys and R. Urtasun
  Box In the Box: Joint 3D Layout and Object Reasoning from Single Images [pdf]
  In International Conference on Computer Vision (ICCV), Sydney, Australia, December 2013


• H. Zhang, A. Geiger and R. Urtasun
  Understanding High-Level Semantics by Modeling Traffic Patterns [pdf]
  In International Conference on Computer Vision (ICCV), Sydney, Australia, December 2013


• J. Zhang, K. Chen, A. Schwing and R. Urtasun
  Estimating the 3D Layout of Indoor Scenes and its Clutter from Depth Sensors [pdf]
  In International Conference on Computer Vision (ICCV), Sydney, Australia, December 2013


• M. Brubaker, A. Geiger and R. Urtasun
  Lost! Leveraging the Crowd for Probabilistic Visual Self-Localization [pdf]    (oral, Best Paper Runner up Award)
  In Conference on Computer Vision and Pattern Recognition (CVPR), Portland, USA, June 2013


• K. Yamaguchi, D. McAllester and R. Urtasun
  Robust Monocular Epipolar Flow Estimation [pdf] (oral)
  In Conference on Computer Vision and Pattern Recognition (CVPR), Portland, USA, June 2013


• S. Fidler, R. Mottaghi, A. Yuille and R. Urtasun
  Bottom-up Segmentation for Top-down Detection [pdf]
  In Conference on Computer Vision and Pattern Recognition (CVPR), Portland, USA, June 2013


• S. Fidler, A. Sharma and R. Urtasun
  A Sentence is Worth a Thousand Pixels [pdf]
  In Conference on Computer Vision and Pattern Recognition (CVPR), Portland, USA, June 2013


• R. Mottaghi, S. Fidler, J. Yao, R. Urtasun and D. Parikh
  Analyzing Semantic Segmentation Using Human-Machine Hybrid CRFs [pdf]
  In Conference on Computer Vision and Pattern Recognition (CVPR), Portland, USA, June 2013

2012

• S. Fidler, S. Dickinson and R. Urtasun
  3D Object Detection and Viewpoint Estimation with a Deformable 3D Cuboid Model  (spotlight)
  In Neural Information Processing Systems (NIPS), Lake Tahoe, USA, December 2012


• A. Schwing, T. Hazan, M. Pollefeys and R. Urtasun
  Globally Convergent Dual MAP LP Relaxation Solvers using Fenchel-Young Margins
  In Neural Information Processing Systems (NIPS), Lake Tahoe, USA, December 2012


• K. Yamaguchi, T. Hazan, D. McAllester and R. Urtasun
  Continuous Markov Random Fields for Robust Stereo Estimation  (oral)
  In European Conference on Computer Vision (ECCV), Florence, Italy, October 2012


• A. Schwing and R. Urtasun
  Efficient Exact Inference for 3D Indoor Scene Understanding
  In European Conference on Computer Vision (ECCV), Florence, Italy, October 2012


• M. Salzmann and R. Urtasun
  Beyond Feature Points: Structured Prediction for Monocular Non-rigid 3D Reconstruction
  In European Conference on Computer Vision (ECCV), Florence, Italy, October 2012


• A. Schwing, T. Hazan, M. Pollefeys and R. Urtasun
  Efficient Structured Prediction with Latent Variables for General Graphical Models  (oral)
  In International Conference on Machine Learning (ICML), Edimburgh, Scotland, June 2012


• A. Geiger, P. Lenz and R. Urtasun
  Are we ready for autonomous driving? The KITTI Vision Benchmark Suite [pdf] [project page] (oral)
  In Conference on Computer Vision and Pattern Recognition (CVPR), Providence, USA, June 2012


• J. Yao, S. Fidler and R. Urtasun
  Describing the Scene as a Whole: Joint Object Detection, Scene Classification and Semantic Segmentation
  In Conference on Computer Vision and Pattern Recognition (CVPR), Providence, USA, June 2012


• A. Schwing, T. Hazan, M. Pollefeys and R. Urtasun
  Efficient Structured Prediction for 3D Indoor Scene Understanding
  In Conference on Computer Vision and Pattern Recognition (CVPR), Providence, USA, June 2012


• A. Varol, M. Salzmann, P. Fua and R. Urtasun
  A Constrained Latent Variable Model
  In Conference on Computer Vision and Pattern Recognition (CVPR), Providence, USA, June 2012


• M. Brubaker, M. Salzmann and R. Urtasun
  A Family of MCMC Methods on Implicitly Defined Manifolds
  In International Conference on Artificial Intelligence and Statistics (AISTATS), Gran Canaria, Spain, April 2012

2011

• A. Geiger, C. Wojek and R. Urtasun
  Joint 3D Estimation of Objects and Scene Layout [pdf] [supplementary] [video]
  In Neural Information Processing Systems (NIPS), Granada, Spain, December 2011


• A. Yao, J. Gall, L. van Gool and R. Urtasun
  Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities [pdf] [video learning] [video humaneva] [software]
  In Neural Information Processing Systems (NIPS), Granada, Spain, December 2011


• M. Salzmann and R. Urtasun
  Physically-based Motion Models for 3D Tracking: A Convex Formulation
  In International Conference on Computer Vision (ICCV), Barcelona, Spain, November 2011


• T. Hazan and R. Urtasun
  Approximated Structured Prediction for Learning Large Scale Graphical Models
  Arxiv 1006.2899, June 2010


• J. Peng, T. Hazan, D. McAllester and R. Urtasun
  Convex Max-Product over Compact Sets for Protein Folding  (oral)
  In International Conference in Machine Learning (ICML), Bellevue, Washington, June 2011


• A. Geiger, M. Lauer and R. Urtasun
  A generative model for 3D urban scene understanding from movable platforms [pdf] [talk] [slides] [data] [software]  (oral)
  In Conference of Computer Vision and Pattern Recognition (CVPR), Colorado Springs, June 2011


• A. Schwing, T. Hazan, M. Pollefeys and R. Urtasun
  Distributed Message Passing for Large Scale Graphical Models [pdf] [software]
  In Conference of Computer Vision and Pattern Recognition (CVPR), Colorado Springs, June 2011


• A. Shyr, T. Darrell, M. Jordan and R. Urtasun
  Supervised Hierarchical Pitman-Yor Process for Natural Scene Segmentation
  In Conference of Computer Vision and Pattern Recognition (CVPR), Colorado Springs, June 2011


• H. Hamer, J. Gall, R. Urtasun and L. Van Gool
  Data-Driven Animation of Hand- Object Interaction  (oral)
  In Face and Gesture Recognition (FGR), Santa Barbara, April 2011

2010

• A. Kapoor, K. Grauman, R. Urtasun and T. Darrell.
  Gaussian Processes for Object Categorization
  In International Journal in Computer Vision, (IJCV) 2010


• T. Hazan and R. Urtasun
  A Primal-Dual Message-Passing Algorithm for Approximated Large Scale Structured Prediction
  In Neural Information Processing Systems (NIPS), Vancouver, December 2010


• M. Salzmann and R. Urtasun
  Implicitly Constrained Gaussian Process Regression for Monocular Non-Rigid Pose Estimation
  Inn Neural Information Processing Systems (NIPS), Vancouver, December 2010


• T. Kim, G. Shakhnarovich and R. Urtasun
  Sparse coding for learning interpretable spatio-temporal primitivesIn Neural Information Processing Systems (NIPS), Vancouver, December 2010


• A. Geiger, M. Roser and R. Urtasun
  Efficient Large-Scale Stereo Matching [pdf] [talk] [software]  (oral)
  In Asian Conference in Computer Vision (ACCV), Queenstown, New Zealand, November 2010


• C. M. Christoudias, R. Urtasun, M. Salzmann and T. Darrell
  Learning to Recognize Objects from Unseen Modalities [pdf] [project page][software]
  In European Conference in Computer Vision (ECCV), Crete, September 2010


• M. Salzmann and R. Urtasun
  Combining Discriminative and Generative Methods for 3D Deformable Surface and Articulated Pose Reconstruction [pdf] [supplementary]  (oral)
  In Conference in Computer Vision and Pattern Recognition (CVPR), San Francisco, June 2010


• A. Shyr, R. Urtasun and M. I. Jordan
  Sufficient Dimensionality Reduction for Visual Sequence Classification
  In Conference in Computer Vision and Pattern Recognition (CVPR), San Francisco, June 2010


• M. Salzmann, C. Ek, R. Urtasun and T. Darrell
  Factorized Orthogonal Latent Spaces
  In International Conference on Artificial Intelligence and Statistics (AISTATS), Sardinia, Italy, May 2010


• M. Salzmann, C. H. Ek, R. Urtasun and T. Darrell
  FOLS: Factorized Orthogonal Latent Spaces.
  In Learning Workshop (Snowbird) Snowbird, Utah, April 2010
  


• M. Salzmann and R. Urtasun
  A Constrained Combination of Discriminative and Generative Methods.
  In Learning Workshop (Snowbird) Snowbird, Utah, April 2010
  

2009

• N. D. Lawrence and R. Urtasun
  Non-linear Matrix Factorization with Gaussian Processes [pdf] [software]  (oral)
  In International Conference in Machine Learning (ICML), Montreal June 2009


• A. Geiger, R. Urtasun and T. Darrell
  Rank Priors for Continuous Non-Linear Dimensionality Reduction [pdf] [video]
  In Conference in Computer Vision and Pattern Recognition (CVPR), Miami June 2009


• C. M. Christoudias, R. Urtasun, A. Kapoor and T. Darrell
  Co-training with noisy perceptual observations
  In Conference in Computer Vision and Pattern Recognition (CVPR), Miami June 2009


• C. M. Christoudias, R. Urtasun and T. Darrell
  Bayesian Localized Multiple Kernel Learning.
  In Learning from Multiple Sources with Applications to Robotics Workshop at NIPS, Whister, Canada, December 2009
  


• R. Urtasun
  Non-Parametric Latent Variable Models for Shape and Motion Analysis.
  Invited talk in MIRAGE Versailles, France, May 2009
  


• N. D. Lawrence and R. Urtasun
  Non-Linear Matrix Factorization.
  In Learning Workshop (Snowbird) Clearwater, Florida, April 2009
  


• R. Urtasun, A. Geiger and T. Darrell
  Rank Priors for Continuous Non-Linear Dimensionality Reduction.
  In Learning Workshop (Snowbird) Clearwater, Florida, April 2009
  


• C. M. Christoudias, R. Urtasun, A. Kapoor and T. Darrell
  Co-training with Noisy Perceptual Observations.
  In Learning Workshop (Snowbird) Clearwater, Florida, April 2009
  

2008

• C. M. Christoudias, R. Urtasun and T. Darrell
  Multi-View Learning in the Presence of View Disagreement [pdf] [talk]  (oral)
  In Conference on Uncertainty in Artificial Intelligence (UAI) Helsinki, Finland, July 2008


• R. Urtasun, D. J. Fleet, A. Geiger, J. Popovic, T. Darrell and N. D. Lawrence.
  Topologically-Constrained Latent Variable Models. [pdf] [video] [talk]  (oral)
  In International Conference in Machine Learning (ICML) Helsinki, Finland, July 2008


• R. Urtasun and T. Darrell
  Local Probabilistic Regression for Activity-Independent Human Pose Inference
  In Conference in Computer Vision and Pattern Recognition (CVPR) Anchorage, Alaska, June 2008


• M. Salzmann, R. Urtasun and P. Fua
  Local Deformation Models for Monocular 3D Shape Recovery  (oral)
  In Conference in Computer Vision and Pattern Recognition (CVPR) Anchorage, Alaska, June 2008


• C. M. Christoudias, R. Urtasun and T. Darrell
  Unsupervised Distributed Feature Selection for Multi-view Object Recognition
  In Conference in Computer Vision and Pattern Recognition (CVPR) Anchorage, Alaska, June 2008


• R. Urtasun and T. Darrell
  Local Probabilistic Regression for Activity-Independent Human Pose Inference.
  In Learning Workshop (Snowbird) Snowbird, Utah, April 2008
  


• R. Urtasun, A. Quattoni, N. D. Lawrence and T. Darrell
  Transfering Nonlinear Representations using Gaussian Processes with a Shared Latent Space.
  In Learning Workshop (Snowbird) Snowbird, Utah, April 2008
  


• R. Urtasun, A. Quattoni, N. Lawrence and T. Darrell
  Transferring Nonlinear Representations using Gaussian Processes with a Shared Latent Space
  MIT technical report, 2008

2007

• A. Kapoor, K. Grauman, R. Urtasun and T. Darrell
  Active Learning with Gaussian Processes for Object Categorization
  In International Conference in Computer Vision (ICCV) Rio de Janeiro, October 2007


• R. Urtasun and T. Darrell
  Discriminative Gaussian Process Latent Variable Models for Classification (oral)
  International Conference in Machine Learning (ICML) Oregon, June 2007


• R. Urtasun, D. J. Fleet and N. D. Lawrence
  Modeling human locomotion with topologically constrained latent variable models.
  In ICCV Workshop on Human Motion: Understanding, Modeling, Capture and Animation, Rio de Janeiro, Brazil, October 2007
  

2006

• R. Urtasun.
  Motion Models for Robust 3D Human Body Tracking
  Phd Thesis 3541, EPFL 2006
  
  
• R. Urtasun, D. J. Fleet and P. Fua.
  Temporal Motion Models for Monocular and Multiview 3D Human Body Tracking
  In Computer Vision and Image Understanding special issue Modeling People, (CVIU) 2006


• R. Urtasun, D. J.Fleet and P. Fua.
  Gaussian Process Dynamical Models for 3D people tracking. [pdf] [video_tracking] [video_model]
  In Conference on Computer Vision and Pattern Recognition (CVPR) New York, June 2006


• R. Urtasun, D. J. Fleet, A. Hertzmann and P. Fua
  Gaussian Processes for Monocular 3D People tracking.
  In Gaussian Processes in Practice Workshop Bletchley Park, U.K, June 2006
  

2005

• L. Herda, R. Urtasun and P. Fua.
  Hierarchical Implicit Surface Joint Limits for Human Body Tracking.
  In Computer Vision and Image Understanding, (CVIU) 2005


• R. Urtasun, D. J.Fleet, A. Hertzmann and P. Fua.
  Priors for People Tracking from Small Training Sets. [pdf] [video_tracking]  (oral)
  In International Conference in Computer Vision (ICCV) Beijing, China, October 2005


• R. Urtasun, D. J.Fleet and P. Fua.
  Monocular 3D Tracking of the Golf Swing.
  In Conference on Computer Vision and Pattern Recognition (CVPR) San Diego, CA, June 2005

2004

• R. Urtasun, P. Glardon, R. Boulic, D. Thalmann and P. Fua.
  Style-based Motion Synthesis. [pdf] [video]
  In Computer Graphics Forum (CGF), Vol. 23, number 4 pp 799-812. December 2004


• R. Urtasun, P. Fua.
  3D Human Body Tracking using Deterministic Motion Models.
  In European Conference on Computer Vision (ECCV), Prague, Czech Republic, May 2004


• L.Herda, R. Urtasun, P. Fua.
  Hierarchical Implicit Surface Joint Limits to Constrain Video-Based Motion Capture.
  In European Conference on Computer Vision (ECCV), Prague, Czech Republic, May 2004


• R. Urtasun, P. Fua.
  3D Tracking for Gait Characterization and Recognition.  (oral)
  In Proceeding of the 6th International Conference on Automatic Face and Gesture Recognition (FGR), Seoul, Korea, May 2004. IEEE Computer Society.


• R. Urtasun M. Salzmann and P. Fua
  3D Morphing without User Interaction
  EPFL technical report, 2004

2003

• L.Herda, R.Urtasun, P.Fua, A.Hanson.
  Automatic Determination of Shoulder Joint Limits using Quaternion Field Boundaries.
  International Journal of Robotics Research (IJRR), 22(6): 419 - 436, 2003.


• P. Dokladal, I. Bloch, M. Couprie, D. Ruijters, R. Urtasun and L. Garnero.
  Topologically Controlled Segmentation of 3D Magnetic Resonance Images of the Head by using Morphological Operators.
  Pattern Recognition, 36(10):2463 - 2478, 2003.

2002

• L.Herda, R.Urtasun, P.Fua and A.Hanson.
  An Automatic Method for Determining Quaternion Field Boundaries for Ball-and-Socket Joint Limits.
  In Proceedings of the 5th International Conference on Automatic Face and Gesture Recognition (FGR), pages 95 - 100, Washington DC, May 2002. IEEE Computer Society.

2001

• P. Dokladal, R. Urtasun, I. Bloch and L. Garnero.
  Segmentation of 3D head MR images using Morphological reconstruction under constraints and automatic selection of markers.
  In International Conference on Image Processing (ICIP), pages 1075-1078, Thessaloniki, Greece, October 2001
  

2000

• R. Urtasun
  Automatic segmentation of a fix number of markers (apply to the cerebellum and brainstem)
  Telecom Paris technical report, 2000


• R. Urtasun
  Segmentation of a Guinea pig using mathematical morphology
  Telecom Paris technical report, 2000


• R. Urtasun
  Implementation of a tool to Visualize Protocol Design and Processing
  Eurecom's technical report, 2000