activity
20172021
most citedLearning to Prune Filters in Convolutional Neural Networks

27 citations · 60 across the 4 of their papers we have counts for

collaborators

5 papers

cs.RO2021

SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies

Matt Vitelli, Yan Chang, Yawei Ye +7

In this paper we present the first safe system for full control of self-driving vehicles trained from human demonstrations and deployed in challenging, real-world, urban environmen…

cs.CV2018

Recurrent Slice Networks for 3D Segmentation of Point Clouds

Qiangui Huang, Weiyue Wang, Ulrich Neumann

Point clouds are an efficient data format for 3D data. However, existing 3D segmentation methods for point clouds either do not model local dependencies \cite{pointnet} or require…

cs.CV201827 cited

Learning to Prune Filters in Convolutional Neural Networks

Qiangui Huang, Kevin Zhou, Suya You +1

Many state-of-the-art computer vision algorithms use large scale convolutional neural networks (CNNs) as basic building blocks. These CNNs are known for their huge number of parame…

cs.CV201724 cited

Shape Inpainting using 3D Generative Adversarial Network and Recurrent Convolutional Networks

Weiyue Wang, Qiangui Huang, Suya You +2

Recent advances in convolutional neural networks have shown promising results in 3D shape completion. But due to GPU memory limitations, these methods can only produce low-resoluti…

cs.CV20179 cited

Automatic Vertebra Labeling in Large-Scale 3D CT using Deep Image-to-Image Network with Message Passing and Sparsity Regularization

Dong Yang, Tao Xiong, Daguang Xu +10

Automatic localization and labeling of vertebra in 3D medical images plays an important role in many clinical tasks, including pathological diagnosis, surgical planning and postope…