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Raquel Urtasun

Raquel Urtasun
3 BLOG ARTICLES 35 RESEARCH PAPERS
Raquel Urtasun is the Head of Uber ATG Toronto, as well as an Associate Professor in the Department of Computer Science at the University of Toronto and a co-founder of the Vector Institute for AI.

Engineering Blog Articles

Announcing the 2019 Uber AI Residency

The Uber AI Residency is a 12-month training program for academics and professionals interested in becoming an AI researcher with Uber AI Labs or Uber ATG.

Introducing the Uber AI Residency

Interested in accelerating your career by tackling some of Uber’s most challenging AI problems? Apply for the Uber AI Residency, a research fellowship dedicated to fostering the next generation of AI talent.

SBNet: Leveraging Activation Block Sparsity for Speeding up Convolutional Neural Networks

Uber ATG Toronto developed Sparse Blocks Network (SBNet), an open source algorithm for TensorFlow, to speed up inference of our 3D vehicle detection systems while lowering computational costs.

Research Papers

LanczosNet: Multi-Scale Deep Graph Convolutional Networks

R. Liao, Z. Zhao, R. Urtasun, R. Zemel
Relational data can generally be represented as graphs. For processing such graph structured data, we propose LanczosNet, which uses the Lanczos algorithm to construct low rank approximations of the graph Laplacian for graph convolution. [...] [PDF at University of Toronto]
Neural Information Processing Systems (NeurIPS), 2018

SurfConv: Bridging 3D and 2D Convolution for RGBD Images

H. Chu, W. Ma, K. Kundu, R. Urtasun, S. Fidler
The last few years have seen approaches trying to combine the increasing popularity of depth sensors and the success of the convolutional neural networks. Using depth as additional channel alongside the RGB input has the scale variance problem present in image convolution based approaches. [...] [PDF at Computer Vision Foundation open access]
Conference on Computer Vision and Pattern Recognition (CVPR), 2018

Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a...

W. Luo, B. Yang, R. Urtasun
In this paper we propose a novel deep neural network that is able to jointly reason about 3D detection, tracking and motion forecasting given data captured by a 3D sensor. By jointly reasoning about these tasks, our holistic approach is more robust to occlusion as well as sparse data at range. [...] [PDF at Computer Vision Foundation open access]
Conference on Computer Vision and Pattern Recognition (CVPR), 2018

Learning to Localize Using a LiDAR Intensity Map

I. Bârsan, S. Wang, A. Pokrovsky, R. Urtasun
In this paper we propose a real-time, calibration-agnostic and effective localization system for self-driving cars. Our method learns to embed the online LiDAR sweeps and intensity map into a joint deep embedding space. [...] [PDF at Proceedings of Machine Learning Research]
Conference on Robot Learning (CORL), 2018

HDNET: Exploiting HD Maps for 3D Object Detection

B. Yang, M. Liang, R. Urtasun
In this paper we show that High-Definition (HD) maps provide strong priors that can boost the performance and robustness of modern 3D object detectors. Towards this goal, we design a single stage detector that extracts geometric and semantic features from the HD maps. [...] [PDF at Proceedings of Machine Learning Research]
Conference on Robot Learning (CORL), 2018

Deep Continuous Fusion for Multi-Sensor 3D Object Detection

M. Liang, B. Yang, S. WangR. Urtasun
In this paper, we propose a novel 3D object detector that can exploit both LIDAR as well as cameras to perform very accurate localization. Towards this goal, we design an end-to-end learnable architecture that exploits continuous convolutions to fuse image and LIDAR feature maps at different levels of resolution. [...] [PDF at google.drive.com]
European Conference on Computer Vision (ECCV), 2018

IntentNet: Learning to Predict Intention from Raw Sensor Data

S. Casas, W. Luo, R. Urtasun
In order to plan a safe maneuver, self-driving vehicles need to understand the intent of other traffic participants. We define intent as a combination of discrete high level behaviors as well as continuous trajectories describing future motion. In this paper we develop a one-stage detector and forecaster that exploits both 3D point clouds produced by a LiDAR sensor as well as dynamic maps of the environment. [...] [PDF at Proceedings of Machine Learning Research]
Conference on Robot Learning (CORL), 2018

End-to-End Deep Structured Models for Drawing Crosswalks

J. Liang, R. Urtasun
In this paper we address the problem of detecting crosswalks from LiDAR and camera imagery. Towards this goal, given multiple Li-DAR sweeps and the corresponding imagery, we project both inputs onto the ground surface to produce a top down view of the scene. [...] [PDF at drive.google.com]
European Conference on Computer Vision (ECCV), 2018

Neural Guided Constraint Logic Programming for Program Synthesis

Lisa Z., G. Rosenblatt, E. Fetaya, R. Liao, W. Byrd, M. Might, R. Urtasun, R. Zemel
Synthesizing programs using example input/outputs is a classic problem in artificial intelligence. We present a method for solving Programming By Example (PBE) problems by using a neural model to guide the search of a constraint logic programming system called miniKanren. [...] [PDF at arXiv]
Advances in Neural Information Processing Systems (NeurIPS), 2018

Efficient Convolutions for Real-Time Semantic Segmentation of 3D Point Clouds

C. Zhang, W. Luo, R. Urtasun
We propose an approach for semi-automatic annotation of object instances. While most current methods treat object segmentation as a pixel-labeling problem, we here cast it as a polygon prediction task, mimicking how most current datasets have been annotated. [...] [PDF at University of Toronto]
International Conference on 3D Vision (3DV), 2018

Single Image Intrinsic Decomposition Without a Single Intrinsic Image

W. Ma, H, Chu, B. Zhou, R. Urtasun, A. Torralba
We propose an approach for semi-automatic annotation of object instances. While most current methods treat object segmentation as a pixel-labeling problem, we here cast it as a polygon prediction task, mimicking how most current datasets have been annotated. [...] [PDF at MIT]
European Conference on Computer Vision (ECCV), 2018

GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation

X. Qi, R. Liao, Z. Liu, R. Urtasun, J. Jia
In this paper, we propose Geometric Neural Network (GeoNet) to jointly predict depth and surface normal maps from a single image. Building on top of two-stream CNNs, our GeoNet incorporates geometric relation between depth and surface normal via the new depth-to-normal and normal-to-depth networks. [...] [PDF at Computer Science Department of University of Toronto]
Conference on Computer Vision and Pattern Recognition (CVPR), 2018

Matching Adversarial Networks

G. Mattyus, R. Urtasun
Generative Adversarial Nets (GANs) and Conditonal GANs (CGANs) show that using a trained network as loss function (discriminator) enables to synthesize highly structured outputs (e.g. natural images). However, applying a discriminator network as a universal loss function for common supervised tasks (e.g. semantic segmentation, line detection, depth estimation) is considerably less successful. [...] [PDF at arXiv]
Conference on Computer Vision and Pattern Recognition (CVPR), 2018

Deep Parametric Continuous Convolutional Neural Networks

S. Wang, S. Suo, W. Ma, A. PokrovskyR. Urtasun
We propose an approach for semi-automatic annotation of object instances. While most current methods treat object segmentation as a pixel-labeling problem, we here cast it as a polygon prediction task, mimicking how most current datasets have been annotated. [...] [PDF at Computer Vision Foundation open access]
Conference on Computer Vision and Pattern Recognition (CVPR), 2018

End-to-end Learning of Multi-sensor 3D Tracking by Detection

D. Frossard, R. Urtasun
In this paper we propose a novel approach to tracking by detection that can exploit both cameras as well as LIDAR data to produce very accurate 3D trajectories. Towards this goal, we formulate the problem as a linear program that can be solved exactly, and learn convolutional networks for detection as well as matching in an end-to-end manner. [...] [PDF at arXiv]
International Conference on Robotics and Automation (ICRA), 2018

Hierarchical Recurrent Attention Networks for Structured Online Maps

N. Homayounfar, W. Ma, S. Lakshmikanth, R. Urtasun
In this paper, we tackle the problem of online road network extraction from sparse 3D point clouds. Our method is inspired by how an annotator builds a lane graph, by first identifying how many lanes there are and then drawing each one in turn. [...] [PDF at Computer Vision Foundation open access]
Conference on Computer Vision and Pattern Recognition (CVPR), 2018

PIXOR: Real-time 3D Object Detection from Point Clouds

B. Yang, W. Luo, R. Urtasun
We address the problem of real-time 3D object detection from point clouds in the context of autonomous driving. [...] [PDF at Computer Vision Foundation open access]
Conference on Computer Vision and Pattern Recognition (CVPR), 2018

Sports Field Localization via Deep Structured Models

N. Homayounfar, S. Fidler, R. Urtasun
In this work, we propose a novel way of efficiently localizing a soccer field from a single broadcast image of the game. Related work in this area relies on manually annotating a few key frames and extending the localization to similar images, or installing fixed specialized cameras in the stadium from which the layout of the field can be obtained. [...] [PDF at MIT]
Conference on Computer Vision and Pattern Recognition (CVPR), 2017

Learning deep structured active contours end-to-end

D. Marcos, D. Tuia, B. Kellenberger, L. Zhang, M. Bai, R. Liao, R. Urtasun
The world is covered with millions of buildings, and precisely knowing each instance's position and extents is vital to a multitude of applications. Recently, automated building footprint segmentation models have shown superior detection accuracy thanks to the usage of Convolutional Neural Networks (CNN). [...] [PDF at Computer Vision Foundation open access]
Conference on Computer Vision and Pattern Recognition (CVPR), 2018

Inference in Probabilistic Graphical Models by Graph Neural Networks

K. Yoon, R. Liao, Y. Xiong, L. Zhang, E. Fetaya, R. Urtasun, R. Zemel, X. Pitkow
A fundamental computation for statistical inference and accurate decision-making is to compute the marginal probabilities or most probable states of task-relevant variables. Probabilistic graphical models can efficiently represent the structure of such complex data, but performing these inferences is generally difficult. [...] [PDF at arXiv]
International Conference on Learning Representations (ICLR), 2018

Reviving and Improving Recurrent Back Propagation

R. Liao, Y. Xiong, E. Fetaya, L. Zhang, K. Yoon, X. Pitkow, R. Urtasun, R. Zemel
In this paper, we revisit the recurrent back-propagation (RBP) algorithm, discuss the conditions under which it applies as well as how to satisfy them in deep neural networks. We show that RBP can be unstable and propose two variants based on conjugate gradient on the normal equations (CG-RBP) and Neumann series (Neumann-RBP). [...] [PDF at arXiv]
Conference on Computer Vision and Pattern Recognition (ICML), 2018

Learning to Reweight Examples for Robust Deep Learning

M. Ren, W. Zeng, B. Yang, R. Urtasun
Deep neural networks have been shown to be very powerful modeling tools for many supervised learning tasks involving complex input patterns. However, they can also easily overfit to training set biases and label noises. [...] [PDF at arXiv]
Conference on Computer Vision and Pattern ( ICML), 2018

Graph Partition Neural Networks for Semi-Supervised Classification

R. Liao, M. Brockschmidt, D. Tarlow, A. Gaunt, R. Urtasun, R. Zemel
We present graph partition neural networks (GPNN), an extension of graph neural networks (GNNs) able to handle extremely large graphs. GPNNs alternate between locally propagating information between nodes in small subgraphs and globally propagating information between the subgraphs. [...] [PDF at arXiv]
International Conference on Machine Learning (ICLR Workshop), 2018

Leveraging Constraint Logic Programming for Neural Guided Program Synthesis

L. Zhang, G. Rosenblatt, E. Fetaya, R. Liao, W. Byrd, R. Urtasun, R. Zemel
We present a method for solving Programming by Example (PBE) problems that tightly integrates a neural network with a constraint logic programming system called miniKanren. Internally, miniKanren searches for a program that satisfies the recursive constraints imposed by the provided examples. [...] [PDF at OpenReview.net]
International Conference on Machine Learning (ICLR), 2018

SBNet: Sparse Block’s Network for Fast Inference

M. Ren, A. Pokrovsky, B. Yang, R. Urtasun
Conventional deep convolutional neural networks (CNNs) apply convolution operators uniformly in space across all feature maps for hundreds of layers - this incurs a high computational cost for real-time applications. For many problems such as object detection and semantic segmentation, we are able to obtain a low-cost computation mask, either from a priori problem knowledge, or from a low-resolution segmentation network. [...] [PDF at arXiv]
Conference on Computer Vision and Pattern Recognition (CVPR), 2018

The Reversible Residual Network: Backpropagation Without Storing Activations

A. Gomez, M. Ren, Raquel Urtasun, R. Grosse
Residual Networks (ResNets) have demonstrated significant improvement over traditional Convolutional Neural Networks (CNNs) on image classification, increasing in performance as networks grow both deeper and wider. However, memory consumption becomes a bottleneck as one needs to store all the intermediate activations for calculating gradients using backpropagation. [...] [PDF at NeurIPS Proceedings]
Advances in Neural Information Processing Systems (NIPS), 2017

Be Your Own Prada: Fashion Synthesis With Structural Coherence

S. Zhu, R. Urtasun, S. Fidler, D. Lin, C. Loy
We present a novel and effective approach for generating new clothing on a wearer through generative adversarial learning. Given an input image of a person and a sentence describing a different outfit, our model "redresses" the person as desired, while at the same time keeping the wearer and her/his pose unchanged. [...] [PDF at arXiv]
International Conference on Computer Vision (ICCV), 2017

3D Graph Neural Networks for RGBD Semantic Segmentation

X. Qi, R. Liao, J. Jia, S. Fidler, R. Urtasun
RGBD semantic segmentation requires joint reasoning about 2D appearance and 3D geometric information. In this paper we propose a 3D graph neural network (3DGNN) that builds a k-nearest neighbor graph on top of 3D point cloud. [...] [PDF at University of Toronto]
International Conference on Computer Vision (ICCV), 2017

SGN: Sequential Grouping Networks for Instance Segmentation

S. Liu, J. Jia, S. Fidler, R. Urtasun
In this paper, we propose Sequential Grouping Networks (SGN) to tackle the problem of object instance segmentation. SGNs employ a sequence of neural networks, each solving a sub-grouping problem of increasing semantic complexity in order to gradually compose objects out of pixels. [...] [PDF at University of Toronto]
International Conference on Computer Vision (ICCV), 2017

Situation Recognition With Graph Neural Networks

R. Li, M. Tapaswi, R. Liao, J. Jia, R. Urtasun, S. Fidler
We address the problem of recognizing situations in images. Given an image, the task is to predict the most salient verb (action), and fill its semantic roles such as who is performing the action, what is the source and target of the action, etc. [...] [PDF at arXiv]
International Conference on Computer Vision (ICCV), 2017

Few-Shot Learning Through an Information Retrieval Lens

E. Triantafillou, R. Zemel, R. Urtasun
Few-shot learning refers to understanding new concepts from only a few examples. We propose an information retrieval-inspired approach for this problem that is motivated by the increased importance of maximally leveraging all the available information in this low-data regime. [PDF at NIPS Proceedings]
Code: [LINK at GitHub]
Advances in Neural Information Processing Systems (NIPS), 2017

Annotating Object Instances with a Polygon-RNN

L. Castrejón, K. Kundu, R. Urtasun, S. Fidler
We propose an approach for semi-automatic annotation of object instances. While most current methods treat object segmentation as a pixel-labeling problem, we here cast it as a polygon prediction task, mimicking how most current datasets have been annotated. [...] [PDF at University of Toronto]
Conference on Computer Vision and Pattern Recognition (CVPR), 2017

Towards Diverse and Natural Image Descriptions via a Conditional GAN

B. Dai, S. Fidler, R. Urtasun, D. Lin
In this paper we introduce the TorontoCity benchmark, which covers the full greater Toronto area (GTA) with 712.5 km² of land, 8439 km of road and around 400,000 buildings. Our benchmark provides different perspectives of the world captured from airplanes, drones and cars driving around the city. [...] [PDF at ArXiv]
International Conference on Computer Vision (ICCV), 2017

TorontoCity: Seeing the World With a Million Eyes

S. Wang; M. Bai; G. Mattyus; H. Chu; W. Luo; B. Yang; J. Liang; J. Cheverie; S. Fidler; R. Urtasun; D. Lin.
Despite the substantial progress in recent years, the image captioning techniques are still far from being perfect. Sentences produced by existing methods, e.g. those based on RNNs, are often overly rigid and lacking in variability. [...] [PDF at arXiv]
International Conference on Computer Vision (ICCV), 2017

Deep Watershed Transform for Instance Segmentation

M. Bai, R. Urtasun
Most contemporary approaches to instance segmentation use complex pipelines involving conditional random fields, recurrent neural networks, object proposals, or template matching schemes. In our paper, we present a simple yet powerful end-to-end convolutional neural network to tackle this task. [...] [PDF at arXiv]
Conference on Computer Vision and Pattern Recognition (CVPR), 2017

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