221 citations · 615 across the 11 of their papers we have counts for
30 papers
PseudoAugment: Learning to Use Unlabeled Data for Data Augmentation in Point Clouds
Zhaoqi Leng, Shuyang Cheng, Benjamin Caine +5
Data augmentation is an important technique to improve data efficiency and save labeling cost for 3D detection in point clouds. Yet, existing augmentation policies have so far been…
Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset
Scott Ettinger, Shuyang Cheng, Benjamin Caine +15
As autonomous driving systems mature, motion forecasting has received increasing attention as a critical requirement for planning. Of particular importance are interactive situatio…
Revisiting ResNets: Improved Training and Scaling Strategies
Irwan Bello, William Fedus, Xianzhi Du +5
Novel computer vision architectures monopolize the spotlight, but the impact of the model architecture is often conflated with simultaneous changes to training methodology and scal…
Pseudo-labeling for Scalable 3D Object Detection
Benjamin Caine, Rebecca Roelofs, Vijay Vasudevan +4
To safely deploy autonomous vehicles, onboard perception systems must work reliably at high accuracy across a diverse set of environments and geographies. One of the most common te…
Scaling Local Self-Attention for Parameter Efficient Visual Backbones
Ashish Vaswani, Prajit Ramachandran, Aravind Srinivas +3
Self-attention has the promise of improving computer vision systems due to parameter-independent scaling of receptive fields and content-dependent interactions, in contrast to para…
Scalable Scene Flow from Point Clouds in the Real World
Philipp Jund, Chris Sweeney, Nichola Abdo +2
Autonomous vehicles operate in highly dynamic environments necessitating an accurate assessment of which aspects of a scene are moving and where they are moving to. A popular appro…