Wenyuan Zeng

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Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes

Self-driving vehicles must perceive and predict the future positions of ...
04/08/2021 ∙ by Sean Segal, et al. ∙ 0 ∙ share

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Deep Structured Reactive Planning

An intelligent agent operating in the real-world must balance achieving ...
01/18/2021 ∙ by Jerry Liu, et al. ∙ 0 ∙ share

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End-to-end Interpretable Neural Motion Planner

In this paper, we propose a neural motion planner (NMP) for learning to ...
01/17/2021 ∙ by Wenyuan Zeng, et al. ∙ 0 ∙ share

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LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting

Forecasting the future behaviors of dynamic actors is an important task ...
01/17/2021 ∙ by Wenyuan Zeng, et al. ∙ 42 ∙ share

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Network Automatic Pruning: Start NAP and Take a Nap

Network pruning can significantly reduce the computation and memory foot...
01/17/2021 ∙ by Wenyuan Zeng, et al. ∙ 0 ∙ share

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Auto4D: Learning to Label 4D Objects from Sequential Point Clouds

In the past few years we have seen great advances in 3D object detection...
01/17/2021 ∙ by Bin Yang, et al. ∙ 0 ∙ share

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Self-Supervised Representation Learning from Flow Equivariance

Self-supervised representation learning is able to learn semantically me...
01/16/2021 ∙ by Yuwen Xiong, et al. ∙ 0 ∙ share

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Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting

In this paper, we address the important problem in self-driving of forec...
01/07/2021 ∙ by Katie Luo, et al. ∙ 19 ∙ share

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Perceive, Attend, and Drive: Learning Spatial Attention for Safe Self-Driving

In this paper, we propose an end-to-end self-driving network featuring a...
11/02/2020 ∙ by Bob Wei, et al. ∙ 0 ∙ share

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Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks

Compressing large neural networks is an important step for their deploym...
10/29/2020 ∙ by Julieta Martinez, et al. ∙ 0 ∙ share

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Weakly-supervised 3D Shape Completion in the Wild

3D shape completion for real data is important but challenging, since pa...
08/20/2020 ∙ by Jiayuan Gu, et al. ∙ 11 ∙ share

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V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction

In this paper, we explore the use of vehicle-to-vehicle (V2V) communicat...
08/17/2020 ∙ by Tsun-Hsuan Wang, et al. ∙ 0 ∙ share

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DSDNet: Deep Structured self-Driving Network

In this paper, we propose the Deep Structured self-Driving Network (DSDN...
08/13/2020 ∙ by Wenyuan Zeng, et al. ∙ 5 ∙ share

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End-to-end Contextual Perception and Prediction with Interaction Transformer

In this paper, we tackle the problem of detecting objects in 3D and fore...
08/13/2020 ∙ by Lingyun Luke Li, et al. ∙ 20 ∙ share

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LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World

We tackle the problem of producing realistic simulations of LiDAR point ...
06/16/2020 ∙ by Sivabalan Manivasagam, et al. ∙ 12 ∙ share

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PnPNet: End-to-End Perception and Prediction with Tracking in the Loop

We tackle the problem of joint perception and motion forecasting in the ...
05/29/2020 ∙ by Ming Liang, et al. ∙ 10 ∙ share

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Differentiable Compositional Kernel Learning for Gaussian Processes

The generalization properties of Gaussian processes depend heavily on th...
06/12/2018 ∙ by Shengyang Sun, et al. ∙ 2 ∙ share

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Learning to Reweight Examples for Robust Deep Learning

Deep neural networks have been shown to be very powerful modeling tools ...
03/24/2018 ∙ by Mengye Ren, et al. ∙ 0 ∙ share

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Efficient Summarization with Read-Again and Copy Mechanism

Encoder-decoder models have been widely used to solve sequence to sequen...
11/10/2016 ∙ by Wenyuan Zeng, et al. ∙ 0 ∙ share

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Wenyuan Zeng

Featured Co-authors

Raquel Urtasun

Bin Yang

Sanja Fidler

Hao Su

Roger Grosse

Renjie Liao

Mengye Ren

Ersin Yumer

Zihao Wang

Shenlong Wang

Yun Chen

Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes

Deep Structured Reactive Planning

End-to-end Interpretable Neural Motion Planner

LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting

Network Automatic Pruning: Start NAP and Take a Nap

Auto4D: Learning to Label 4D Objects from Sequential Point Clouds

Self-Supervised Representation Learning from Flow Equivariance

Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting

Perceive, Attend, and Drive: Learning Spatial Attention for Safe Self-Driving

Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks

Weakly-supervised 3D Shape Completion in the Wild

V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction

DSDNet: Deep Structured self-Driving Network

End-to-end Contextual Perception and Prediction with Interaction Transformer

LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World

PnPNet: End-to-End Perception and Prediction with Tracking in the Loop

Differentiable Compositional Kernel Learning for Gaussian Processes

Learning to Reweight Examples for Robust Deep Learning

Efficient Summarization with Read-Again and Copy Mechanism