Search Results for author: Wenyuan Zeng

Found 23 papers, 6 papers with code

Rethinking Closed-loop Training for Autonomous Driving

no code implementations27 Jun 2023 Chris Zhang, Runsheng Guo, Wenyuan Zeng, Yuwen Xiong, Binbin Dai, Rui Hu, Mengye Ren, Raquel Urtasun

Recent advances in high-fidelity simulators have enabled closed-loop training of autonomous driving agents, potentially solving the distribution shift in training v. s.

Autonomous Driving

Weakly Supervised Semantic Segmentation via Alternative Self-Dual Teaching

no code implementations17 Dec 2021 Dingwen Zhang, Wenyuan Zeng, Guangyu Guo, Chaowei Fang, Lechao Cheng, Ming-Ming Cheng, Junwei Han

Current weakly supervised semantic segmentation (WSSS) frameworks usually contain the separated mask-refinement model and the main semantic region mining model.

Knowledge Distillation Weakly supervised Semantic Segmentation +1

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

no code implementations8 Apr 2021 Sean Segal, Nishanth Kumar, Sergio Casas, Wenyuan Zeng, Mengye Ren, Jingkang Wang, Raquel Urtasun

As data collection is often significantly cheaper than labeling in this domain, the decision of which subset of examples to label can have a profound impact on model performance.

Active Learning

Deep Structured Reactive Planning

no code implementations18 Jan 2021 Jerry Liu, Wenyuan Zeng, Raquel Urtasun, Ersin Yumer

An intelligent agent operating in the real-world must balance achieving its goal with maintaining the safety and comfort of not only itself, but also other participants within the surrounding scene.

End-to-end Interpretable Neural Motion Planner

1 code implementation CVPR 2019 Wenyuan Zeng, Wenjie Luo, Simon Suo, Abbas Sadat, Bin Yang, Sergio Casas, Raquel Urtasun

In this paper, we propose a neural motion planner (NMP) for learning to drive autonomously in complex urban scenarios that include traffic-light handling, yielding, and interactions with multiple road-users.

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

no code implementations17 Jan 2021 Bin Yang, Min Bai, Ming Liang, Wenyuan Zeng, Raquel Urtasun

The key idea is to decompose the 4D object label into two parts: the object size in 3D that's fixed through time for rigid objects, and the motion path describing the evolution of the object's pose through time.

3D Object Detection Object

Network Automatic Pruning: Start NAP and Take a Nap

no code implementations17 Jan 2021 Wenyuan Zeng, Yuwen Xiong, Raquel Urtasun

This process is typically time-consuming and requires expert knowledge to achieve good results.

Network Pruning

Self-Supervised Representation Learning from Flow Equivariance

no code implementations ICCV 2021 Yuwen Xiong, Mengye Ren, Wenyuan Zeng, Raquel Urtasun

Motivated by this ability, we present a new self-supervised learning representation framework that can be directly deployed on a video stream of complex scenes with many moving objects.

Instance Segmentation object-detection +5

Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting

no code implementations7 Jan 2021 Katie Luo, Sergio Casas, Renjie Liao, Xinchen Yan, Yuwen Xiong, Wenyuan Zeng, Raquel Urtasun

On two large-scale real-world datasets, nuScenes and ATG4D, we showcase that our scene-occupancy predictions are more accurate and better calibrated than those from state-of-the-art motion forecasting methods, while also matching their performance in pedestrian motion forecasting metrics.

Graph Neural Network Motion Forecasting

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

no code implementations2 Nov 2020 Bob Wei, Mengye Ren, Wenyuan Zeng, Ming Liang, Bin Yang, Raquel Urtasun

In this paper, we propose an end-to-end self-driving network featuring a sparse attention module that learns to automatically attend to important regions of the input.

Motion Planning

Weakly-supervised 3D Shape Completion in the Wild

no code implementations ECCV 2020 Jiayuan Gu, Wei-Chiu Ma, Sivabalan Manivasagam, Wenyuan Zeng, ZiHao Wang, Yuwen Xiong, Hao Su, Raquel Urtasun

3D shape completion for real data is important but challenging, since partial point clouds acquired by real-world sensors are usually sparse, noisy and unaligned.

Point Cloud Registration Pose Estimation

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

3 code implementations ECCV 2020 Tsun-Hsuan Wang, Sivabalan Manivasagam, Ming Liang, Bin Yang, Wenyuan Zeng, James Tu, Raquel Urtasun

In this paper, we explore the use of vehicle-to-vehicle (V2V) communication to improve the perception and motion forecasting performance of self-driving vehicles.

3D Object Detection Motion Forecasting

DSDNet: Deep Structured self-Driving Network

no code implementations ECCV 2020 Wenyuan Zeng, Shenlong Wang, Renjie Liao, Yun Chen, Bin Yang, Raquel Urtasun

In this paper, we propose the Deep Structured self-Driving Network (DSDNet), which performs object detection, motion prediction, and motion planning with a single neural network.

Motion Planning motion prediction +2

LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World

no code implementations CVPR 2020 Sivabalan Manivasagam, Shenlong Wang, Kelvin Wong, Wenyuan Zeng, Mikita Sazanovich, Shuhan Tan, Bin Yang, Wei-Chiu Ma, Raquel Urtasun

We first utilize ray casting over the 3D scene and then use a deep neural network to produce deviations from the physics-based simulation, producing realistic LiDAR point clouds.

MLPrune: Multi-Layer Pruning for Automated Neural Network Compression

no code implementations27 Sep 2018 Wenyuan Zeng, Raquel Urtasun

Model compression can significantly reduce the computation and memory footprint of large neural networks.

Neural Network Compression

Learning to Reweight Examples for Robust Deep Learning

9 code implementations ICML 2018 Mengye Ren, Wenyuan Zeng, Bin Yang, Raquel Urtasun

Deep neural networks have been shown to be very powerful modeling tools for many supervised learning tasks involving complex input patterns.


Efficient Summarization with Read-Again and Copy Mechanism

no code implementations10 Nov 2016 Wenyuan Zeng, Wenjie Luo, Sanja Fidler, Raquel Urtasun

Towards this goal, we first introduce a simple mechanism that first reads the input sequence before committing to a representation of each word.


Incorporating Relation Paths in Neural Relation Extraction

1 code implementation EMNLP 2017 Wenyuan Zeng, Yankai Lin, Zhiyuan Liu, Maosong Sun

Distantly supervised relation extraction has been widely used to find novel relational facts from plain text.

Relation Relation Extraction

Cannot find the paper you are looking for? You can Submit a new open access paper.