What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
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 most cited work include:
- Are we ready for autonomous driving? The KITTI vision benchmark suite (5548 citations)
- Vision meets robotics: The KITTI dataset (3500 citations)
- Skip-Thought Vectors (834 citations)
What are the main themes of her work throughout her whole career to date?
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.
She most often published in these fields:
- Artificial intelligence (82.27%)
- Computer vision (31.28%)
- Machine learning (25.37%)
What were the highlights of her more recent work (between 2019-2021)?
- Artificial intelligence (82.27%)
- Motion (12.07%)
- Computer vision (31.28%)
In recent papers she was focusing on the following fields of study:
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.
Between 2019 and 2021, her most popular works were:
- SpAGNN: Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data (30 citations)
- PnPNet: End-to-End Perception and Prediction With Tracking in the Loop (30 citations)
- LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World (30 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
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.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
