Her primary areas of study are Artificial intelligence, Computer vision, Object detection, Segmentation and Image segmentation. Her studies deal with areas such as Machine learning and Pattern recognition as well as Artificial intelligence. Her Computer vision research is multidisciplinary, incorporating perspectives in Range, Robotics, Representation and Visual odometry.
Her Object detection research incorporates themes from Video tracking, Orientation, Feature and Optical flow. The Segmentation study combines topics in areas such as Semantics and Feature extraction. She combines subjects such as Vertex, Generative grammar and Polygon with her study of Image segmentation.
Her primary areas of investigation include Artificial intelligence, Computer vision, Machine learning, Segmentation and Pattern recognition. All of her Artificial intelligence and Object detection, Artificial neural network, Inference, Object and Deep learning investigations are sub-components of the entire Artificial intelligence study. She focuses mostly in the field of Computer vision, narrowing it down to topics relating to Lidar and, in certain cases, Point cloud.
Her Machine learning research includes elements of Probabilistic logic and Graph. Her biological study deals with issues like Convolutional neural network, which deal with fields such as Algorithm and Convolution. Raquel Urtasun focuses mostly in the field of Pattern recognition, narrowing it down to matters related to Image and, in some cases, Feature.
Raquel Urtasun mainly investigates Artificial intelligence, Motion, Computer vision, Machine learning and Lidar. Her research is interdisciplinary, bridging the disciplines of Pattern recognition and Artificial intelligence. Her Motion research is multidisciplinary, incorporating elements of Graph, End-to-end principle, Real-time computing, Joint and Trajectory.
Raquel Urtasun studied Computer vision and Benchmark that intersect with Scale and Theoretical computer science. Raquel Urtasun interconnects Motion planning and SAFER in the investigation of issues within Machine learning. Her research integrates issues of Artificial neural network, Feature learning and Robustness in her study of Object detection.
Her primary scientific interests are in Artificial intelligence, Motion, Object detection, Machine learning and Lidar. The study incorporates disciplines such as Trajectory and Computer vision in addition to Artificial intelligence. While the research belongs to areas of Computer vision, Raquel Urtasun spends her time largely on the problem of Benchmark, intersecting her research to questions surrounding Scale and Multi sensor.
Her study focuses on the intersection of Object detection and fields such as Exploit with connections in the field of Raster graphics, Implementation, Robustness and Adjacency matrix. The various areas that Raquel Urtasun examines in her Machine learning study include Motion planning and SAFER. Her Lidar study combines topics from a wide range of disciplines, such as Point cloud, Real-time computing and Bitstream.
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Are we ready for autonomous driving? The KITTI vision benchmark suite
Andreas Geiger;Philip Lenz;Raquel Urtasun.
computer vision and pattern recognition (2012)
Vision meets robotics: The KITTI dataset
A Geiger;P Lenz;C Stiller;R Urtasun.
The International Journal of Robotics Research (2013)
Ryan Kiros;Yukun Zhu;Ruslan Salakhutdinov;Richard S. Zemel.
arXiv: Computation and Language (2015)
Efficient large-scale stereo matching
Andreas Geiger;Martin Roser;Raquel Urtasun.
asian conference on computer vision (2010)
The Role of Context for Object Detection and Semantic Segmentation in the Wild
Roozbeh Mottaghi;Xianjie Chen;Xiaobai Liu;Nam-Gyu Cho.
computer vision and pattern recognition (2014)
3D People Tracking with Gaussian Process Dynamical Models
R. Urtasun;D.J. Fleet;P. Fua.
computer vision and pattern recognition (2006)
Efficient Deep Learning for Stereo Matching
Wenjie Luo;Alexander G. Schwing;Raquel Urtasun.
computer vision and pattern recognition (2016)
3D object proposals for accurate object class detection
Xiaozhi Chen;Kaustav Kundu;Yukun Zhu;Andrew Berneshawi.
neural information processing systems (2015)
Understanding the effective receptive field in deep convolutional neural networks
Wenjie Luo;Yujia Li;Raquel Urtasun;Richard S. Zemel.
neural information processing systems (2016)
Describing the scene as a whole: Joint object detection, scene classification and semantic segmentation
Jian Yao;Sanja Fidler;Raquel Urtasun.
computer vision and pattern recognition (2012)
Profile was last updated on December 6th, 2021.
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