R. Urtasun

Publications342
h-index82
Citations33,416
Highly Influential Citations4,379
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Are we ready for autonomous driving? The KITTI vision benchmark suite
TLDR
We take advantage of our autonomous driving platform to develop novel challenging benchmarks for the tasks of stereo, optical flow, visual odometry/SLAM and 3D object detection. Expand
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Vision meets robotics: The KITTI dataset
TLDR
We present a novel dataset captured from a VW station wagon for use in mobile robotics and autonomous driving research. Expand
  • 3,395
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Skip-Thought Vectors
TLDR
We describe an approach for unsupervised learning of a generic, distributed sentence encoder that can produce highly generic sentence representations that are robust and perform well in practice. Expand
  • 1,654
  • 244
  • PDF
Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books
TLDR
This paper aims to align books to their movie releases in order to provide rich descriptive explanations for visual content that go semantically far beyond the captions available in the current datasets. Expand
  • 897
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The Role of Context for Object Detection and Semantic Segmentation in the Wild
TLDR
We propose a novel deformable part-based model, which exploits both local context around each candidate detection as well as global context at the level of the scene. Expand
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Efficient Large-Scale Stereo Matching
TLDR
We propose a novel approach to binocular stereo for fast matching of high-resolution images and show that state-of-the-art performance can be achieved with significant speedups. Expand
  • 706
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Detect What You Can: Detecting and Representing Objects Using Holistic Models and Body Parts
TLDR
We propose a novel approach to i) handle large deformations and partial occlusions in animals (as examples of highly deformable objects), ii) describe them in terms of body parts, and iii) detect them when they are hard to detect (e.g., animals depicted at low resolution). Expand
  • 357
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Learning to Reweight Examples for Robust Deep Learning
TLDR
We propose a novel meta-learning algorithm that learns to assign weights to training examples based on their gradient directions. Expand
  • 396
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Monocular 3D Object Detection for Autonomous Driving
TLDR
The goal of this paper is to perform 3D object detection from a single monocular image in the domain of autonomous driving. Expand
  • 433
  • 71
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Order-Embeddings of Images and Language
TLDR
We introduce a general method for learning ordered representations, and show how it can be applied to a variety of tasks involving images and language. Expand
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