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Vijay Vasudevan

14 accepted papers

2022

Scene Transformer: A unified architecture for predicting future trajectories of multiple agents

Jiquan Ngiam, Vijay Vasudevan, Benjamin Caine, Zhengdong Zhang, Hao-Tien Lewis Chiang, Jeffrey Ling +8
ICLR 2022poster

Predicting the motion of multiple agents is necessary for planning in dynamic environments. This task is challenging for autonomous driving since agents (e.g., vehicles and pedestrians) and their associated behaviors may be diverse and influence one another. Most prior work have focused on predictin…

Cited by 0Source ↗PDF ↗Scholar ↗
2022

When does dough become a bagel? Analyzing the remaining mistakes on ImageNet

Vijay Vasudevan, Benjamin Caine, Raphael Gontijo-Lopes, Sara Fridovich-Keil, Rebecca Roelofs
NeurIPS 2022accept

Image classification accuracy on the ImageNet dataset has been a barometer for progress in computer vision over the last decade. Several recent papers have questioned the degree to which the benchmark remains useful to the community, yet innovations continue to contribute gains to performance, with…

Cited by 63Source ↗PDF ↗code ↗Scholar ↗
2021

Large Scale Interactive Motion Forecasting for Autonomous Driving: The Waymo Open Motion Dataset

Scott Ettinger, Shuyang Cheng, Benjamin Caine, Chenxi Liu, Hang Zhao, Sabeek Pradhan +12
ICCV 2021poster

As autonomous driving systems mature, motion forecasting has received increasing attention as a critical requirement for planning. Of particular importance are interactive situations such as merges, unprotected turns, etc., where predicting individual object motion is not sufficient. Joint predictio…

Cited by 624PDF ↗Scholar ↗
2021

To the Point: Efficient 3D Object Detection in the Range Image With Graph Convolution Kernels

Yuning Chai, Pei Sun, Jiquan Ngiam, Weiyue Wang, Benjamin Caine, Vijay Vasudevan +2
CVPR 2021poster

3D object detection is vital for many robotics applications. For tasks where a 2D perspective range image exists, we propose to learn a 3D representation directly from this range image view. To this end, we designed a 2D convolutional network architecture that carries the 3D spherical coordinates of…

Cited by 87PDF ↗Scholar ↗
2020

Improving 3D Object Detection through Progressive Population Based Augmentation

Shuyang Cheng, Zhaoqi Leng, Ekin Dogus Cubuk, Barret Zoph, Chunyan Bai, Jiquan Ngiam +7
ECCV 2020poster

Data augmentation has been widely adopted for object detection in 3D point clouds. However, all previous related efforts have focused on manually designing specific data augmentation methods for individual architectures. In this work, we present the first attempt to automate the design of data augme…

Cited by 94Source ↗PDF ↗Scholar ↗
2020

Scalability in Perception for Autonomous Driving: Waymo Open Dataset

Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui +17
CVPR 2020poster

The research community has increasing interest in autonomous driving research, despite the resource intensity of obtaining representative real world data. Existing self-driving datasets are limited in the scale and variation of the environments they capture, even though generalization within and bet…

Cited by 3735PDF ↗Scholar ↗
2020

Streaming Object Detection for 3-D Point Clouds

Wei Han, Zhengdong Zhang, Benjamin Caine, Brandon Yang, Christoph Sprunk, Ouais Alsharif +4
ECCV 2020poster

Autonomous vehicles operate in a dynamic environment, where the speed with which a vehicle can perceive and react impacts the safety and efficacy of the system. LiDAR provides a central and prominent sensory modality that informs many existing perceptual systems including object detection, segmentat…

Cited by 28Source ↗PDF ↗code ↗Scholar ↗
2019

AutoAugment: Learning Augmentation Strategies From Data

Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, Quoc V. Le
CVPR 2019oral

Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations are manually designed. In this paper, we describe a simple procedure called AutoAugment to automatically search for improved data augmentation polici…

Cited by 3054PDF ↗Scholar ↗
2019

End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds

Yin Zhou, Pei Sun, Yu Zhang, Dragomir Anguelov, Jiyang Gao, Tom Ouyang +3
CoRL 2019

Recent work on 3D object detection advocates point cloud voxelization in birds-eye view, where objects preserve their physical dimensions and are naturally separable. When represented in this view, however, point clouds are sparse and have highly variable point density, which may cause detectors dif

Cited by 0Source ↗PDF ↗Scholar ↗
2019

MnasNet: Platform-Aware Neural Architecture Search for Mobile

Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard +1
CVPR 2019poster

Designing convolutional neural networks (CNN) for mobile devices is challenging because mobile models need to be small and fast, yet still accurate. Although significant efforts have been dedicated to design and improve mobile CNNs on all dimensions, it is very difficult to manually balance these tr…

Cited by 4089PDF ↗code ↗Scholar ↗
2018

Learning Transferable Architectures for Scalable Image Recognition

Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le
CVPR 2018poster

Developing neural network image classification models often requires significant architecture engineering. In this paper, we study a method to learn the model architectures directly on the dataset of interest. As this approach is expensive when the dataset is large, we propose to search for an arch…

Cited by 7963Source ↗PDF ↗Scholar ↗