53 citations · 117 across the 9 of their papers we have counts for
19 papers
PredNAS: A Universal and Sample Efficient Neural Architecture Search Framework
Liuchun Yuan, Zehao Huang, Naiyan Wang
In this paper, we present a general and effective framework for Neural Architecture Search (NAS), named PredNAS. The motivation is that given a differentiable performance estimatio…
Direct Differentiable Augmentation Search
Aoming Liu, Zehao Huang, Zhiwu Huang +1
Data augmentation has been an indispensable tool to improve the performance of deep neural networks, however the augmentation can hardly transfer among different tasks and datasets…
Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object Tracking
Jiawei He, Zehao Huang, Naiyan Wang +1
Data association across frames is at the core of Multiple Object Tracking (MOT) task. This problem is usually solved by a traditional graph-based optimization or directly learned v…
LiDAR R-CNN: An Efficient and Universal 3D Object Detector
Zhichao Li, Feng Wang, Naiyan Wang
LiDAR-based 3D detection in point cloud is essential in the perception system of autonomous driving. In this paper, we present LiDAR R-CNN, a second stage detector that can general…
Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences
Ziqi Pang, Zhichao Li, Naiyan Wang
Estimating the states of surrounding traffic participants stays at the core of autonomous driving. In this paper, we study a novel setting of this problem: model-free single-object…
1st Place Solutions of Waymo Open Dataset Challenge 2020 -- 2D Object Detection Track
Zehao Huang, Zehui Chen, Qiaofei Li +2
In this technical report, we present our solutions of Waymo Open Dataset (WOD) Challenge 2020 - 2D Object Track. We adopt FPN as our basic framework. Cascade RCNN, stacked PAFPN Ne…