9.7k citations · 12.2k across the 18 of their papers we have counts for
18 papers · 1 filter
Scaling Autoregressive Models for Content-Rich Text-to-Image Generation
Jiahui Yu, Yuanzhong Xu, Jing Yu Koh +14
We present the Pathways Autoregressive Text-to-Image (Parti) model, which generates high-fidelity photorealistic images and supports content-rich synthesis involving complex compos…
CoCa: Contrastive Captioners are Image-Text Foundation Models
Jiahui Yu, Zirui Wang, Vijay Vasudevan +3
Exploring large-scale pretrained foundation models is of significant interest in computer vision because these models can be quickly transferred to many downstream tasks. This pape…
When does dough become a bagel? Analyzing the remaining mistakes on ImageNet
Vijay Vasudevan, Benjamin Caine, Raphael Gontijo-Lopes +2
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…
To the Point: Efficient 3D Object Detection in the Range Image with Graph Convolution Kernels
Yuning Chai, Pei Sun, Jiquan Ngiam +5
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 i…
Scene Transformer: A unified architecture for predicting multiple agent trajectories
Jiquan Ngiam, Benjamin Caine, Vijay Vasudevan +11
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 pedestri…
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…
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…
Streaming Object Detection for 3-D Point Clouds
Wei Han, Zhengdong Zhang, Benjamin Caine +7
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 pr…
Improving 3D Object Detection through Progressive Population Based Augmentation
Shuyang Cheng, Zhaoqi Leng, Ekin Dogus Cubuk +10
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 augmentati…
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla +22
The research community has increasing interest in autonomous driving research, despite the resource intensity of obtaining representative real world data. Existing self-driving dat…
End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds
Yin Zhou, Pei Sun, Yu Zhang +6
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 represen…
StarNet: Targeted Computation for Object Detection in Point Clouds
Jiquan Ngiam, Benjamin Caine, Wei Han +10
Detecting objects from LiDAR point clouds is an important component of self-driving car technology as LiDAR provides high resolution spatial information. Previous work on point-clo…
Searching for MobileNetV3
Andrew Howard, Mark Sandler, Grace Chu +9
We present the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile phone…
Domain Adaptive Transfer Learning with Specialist Models
Jiquan Ngiam, Daiyi Peng, Vijay Vasudevan +3
Transfer learning is a widely used method to build high performing computer vision models. In this paper, we study the efficacy of transfer learning by examining how the choice of…
MnasNet: Platform-Aware Neural Architecture Search for Mobile
Mingxing Tan, Bo Chen, Ruoming Pang +4
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 ha…
Parallel Architecture and Hyperparameter Search via Successive Halving and Classification
Manoj Kumar, George E. Dahl, Vijay Vasudevan +1
We present a simple and powerful algorithm for parallel black box optimization called Successive Halving and Classification (SHAC). The algorithm operates in stages of parallel…
AutoAugment: Learning Augmentation Policies from Data
Ekin D. Cubuk, Barret Zoph, Dandelion Mane +2
Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations are manually designed. In thi…
Learning Transferable Architectures for Scalable Image Recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens +1
Developing neural network image classification models often requires significant architecture engineering. In this paper, we study a method to learn the model architectures directl…