841 citations · 7.7k across the 158 of their papers we have counts for
20 papers · 1 filter
Dense RepPoints: Representing Visual Objects with Dense Point Sets
Ze Yang, Yinghao Xu, Han Xue +5
We present a new object representation, called Dense RepPoints, that utilizes a large set of points to describe an object at multiple levels, including both box level and pixel lev…
PolyTransform: Deep Polygon Transformer for Instance Segmentation
Justin Liang, Namdar Homayounfar, Wei-Chiu Ma +3
In this paper, we propose PolyTransform, a novel instance segmentation algorithm that produces precise, geometry-preserving masks by combining the strengths of prevailing segmentat…
Identifying Unknown Instances for Autonomous Driving
Kelvin Wong, Shenlong Wang, Mengye Ren +2
In the past few years, we have seen great progress in perception algorithms, particular through the use of deep learning. However, most existing approaches focus on a few categorie…
Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data
Sergio Casas, Cole Gulino, Renjie Liao +1
In this paper, we tackle the problem of relational behavior forecasting from sensor data. Towards this goal, we propose a novel spatially-aware graph neural network (SpAGNN) that m…
Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction
Ajay Jain, Sergio Casas, Renjie Liao +4
Self-driving vehicles plan around both static and dynamic objects, applying predictive models of behavior to estimate future locations of the objects in the environment. However, f…
Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles
Abbas Sadat, Mengye Ren, Andrei Pokrovsky +3
The motion planners used in self-driving vehicles need to generate trajectories that are safe, comfortable, and obey the traffic rules. This is usually achieved by two modules: beh…
Learning to Remember from a Multi-Task Teacher
Yuwen Xiong, Mengye Ren, Raquel Urtasun
Recent studies on catastrophic forgetting during sequential learning typically focus on fixing the accuracy of the predictions for a previously learned task. In this paper we argue…
Efficient Graph Generation with Graph Recurrent Attention Networks
Renjie Liao, Yujia Li, Yang Song +6
We propose a new family of efficient and expressive deep generative models of graphs, called Graph Recurrent Attention Networks (GRANs). Our model generates graphs one block of nod…
DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation
Xiaohui Zeng, Renjie Liao, Li Gu +3
In this paper, we propose the differentiable mask-matching network (DMM-Net) for solving the video object segmentation problem where the initial object masks are provided. Relying…
DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch
Shivam Duggal, Shenlong Wang, Wei-Chiu Ma +2
Our goal is to significantly speed up the runtime of current state-of-the-art stereo algorithms to enable real-time inference. Towards this goal, we developed a differentiable Patc…
DSIC: Deep Stereo Image Compression
Jerry Liu, Shenlong Wang, Raquel Urtasun
In this paper we tackle the problem of stereo image compression, and leverage the fact that the two images have overlapping fields of view to further compress the representations.…
Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization
Wei-Chiu Ma, Ignacio Tartavull, Ioan Andrei Bârsan +7
In this paper we propose a novel semantic localization algorithm that exploits multiple sensors and has precision on the order of a few centimeters. Our approach does not require d…
Deformable Filter Convolution for Point Cloud Reasoning
Yuwen Xiong, Mengye Ren, Renjie Liao +2
Point clouds are the native output of many real-world 3D sensors. To borrow the success of 2D convolutional network architectures, a majority of popular 3D perception models voxeli…
DARNet: Deep Active Ray Network for Building Segmentation
Dominic Cheng, Renjie Liao, Sanja Fidler +1
In this paper, we propose a Deep Active Ray Network (DARNet) for automatic building segmentation. Taking an image as input, it first exploits a deep convolutional neural network (C…
Deep Multi-Sensor Lane Detection
Min Bai, Gellert Mattyus, Namdar Homayounfar +3
Reliable and accurate lane detection has been a long-standing problem in the field of autonomous driving. In recent years, many approaches have been developed that use images (or v…
DeepSignals: Predicting Intent of Drivers Through Visual Signals
Davi Frossard, Eric Kee, Raquel Urtasun
Detecting the intention of drivers is an essential task in self-driving, necessary to anticipate sudden events like lane changes and stops. Turn signals and emergency flashers comm…
Deep Rigid Instance Scene Flow
Wei-Chiu Ma, Shenlong Wang, Rui Hu +2
In this paper we tackle the problem of scene flow estimation in the context of self-driving. We leverage deep learning techniques as well as strong priors as in our application dom…
PIXOR: Real-time 3D Object Detection from Point Clouds
Bin Yang, Wenjie Luo, Raquel Urtasun
We address the problem of real-time 3D object detection from point clouds in the context of autonomous driving. Computation speed is critical as detection is a necessary component…
UPSNet: A Unified Panoptic Segmentation Network
Yuwen Xiong, Renjie Liao, Hengshuang Zhao +4
In this paper, we propose a unified panoptic segmentation network (UPSNet) for tackling the newly proposed panoptic segmentation task. On top of a single backbone residual network,…
LanczosNet: Multi-Scale Deep Graph Convolutional Networks
Renjie Liao, Zhizhen Zhao, Raquel Urtasun +1
We propose the Lanczos network (LanczosNet), which uses the Lanczos algorithm to construct low rank approximations of the graph Laplacian for graph convolution. Relying on the trid…