All Graphs Lead to Rome: Learning Geometric and Cycle-Consistent Representations with Graph Convolutional Networks

7 January 2019

Papers citing "All Graphs Lead to Rome: Learning Geometric and Cycle-Consistent Representations with Graph Convolutional Networks"

10 / 10 papers shown

Title
Relational inductive biases, deep learning, and graph networks Peter W. Battaglia Jessica B. Hamrick V. Bapst Alvaro Sanchez-Gonzalez V. Zambaldi ... Pushmeet Kohli M. Botvinick Oriol Vinyals Yujia Li Razvan Pascanu AI4CE NAI 773 3,132 0 04 Jun 2018
Group Normalization Yuxin Wu Kaiming He 249 3,676 0 22 Mar 2018
Learning to Find Good Correspondences K. M. Yi Eduard Trulls Y. Ono Vincent Lepetit Mathieu Salzmann Pascal Fua 3DV 76 480 0 16 Nov 2017
Distributable Consistent Multi-Object Matching Nan Hu Qixing Huang Boris Thibert Leonidas Guibas 65 10 0 22 Nov 2016
DSAC - Differentiable RANSAC for Camera Localization Eric Brachmann Alexander Krull Sebastian Nowozin Jamie Shotton Frank Michel Stefan Gumhold Carsten Rother 85 598 0 17 Nov 2016
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering M. Defferrard Xavier Bresson P. Vandergheynst GNN 373 7,680 0 30 Jun 2016
Multi-Image Matching via Fast Alternating Minimization Xiaowei Zhou Menglong Zhu Kostas Daniilidis 98 160 0 19 May 2015
Learning to Compare Image Patches via Convolutional Neural Networks Sergey Zagoruyko N. Komodakis SSL 99 1,436 0 14 Apr 2015
ORB-SLAM: a Versatile and Accurate Monocular SLAM System Raul Mur-Artal José M.M. Montiel Juan D. Tardós 133 6,421 0 03 Feb 2015
Spectral Networks and Locally Connected Networks on Graphs Joan Bruna Wojciech Zaremba Arthur Szlam Yann LeCun GNN 247 4,884 0 21 Dec 2013