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Co-Attention for Conditioned Image Matching

Co-Attention for Conditioned Image Matching

16 July 2020
Olivia Wiles
Sébastien Ehrhardt
Andrew Zisserman
    VLM
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Papers citing "Co-Attention for Conditioned Image Matching"

12 / 12 papers shown
Title
Uncertainty-Driven Dense Two-View Structure from Motion
Uncertainty-Driven Dense Two-View Structure from Motion
Weirong Chen
Suryansh Kumar
F. I. F. Richard Yu
19
7
0
01 Feb 2023
A Critical Analysis of Image-based Camera Pose Estimation Techniques
A Critical Analysis of Image-based Camera Pose Estimation Techniques
Mengze Xu
Youchen Wang
Binfeng Xu
Jun Zhang
Jian Ren
S. Poslad
Pengfei Xu
31
15
0
15 Jan 2022
Feature matching for multi-epoch historical aerial images
Feature matching for multi-epoch historical aerial images
Lulin Zhang
E. Rupnik
M. Pierrot-Deseilligny
23
24
0
08 Dec 2021
PDC-Net+: Enhanced Probabilistic Dense Correspondence Network
PDC-Net+: Enhanced Probabilistic Dense Correspondence Network
Prune Truong
Martin Danelljan
Radu Timofte
Luc Van Gool
26
83
0
28 Sep 2021
Deep Matching Prior: Test-Time Optimization for Dense Correspondence
Deep Matching Prior: Test-Time Optimization for Dense Correspondence
Sunghwan Hong
Seungryong Kim
34
26
0
06 Jun 2021
Robust Reference-based Super-Resolution via C2-Matching
Robust Reference-based Super-Resolution via C2-Matching
Yuming Jiang
Kelvin C. K. Chan
Xintao Wang
Chen Change Loy
Ziwei Liu
18
89
0
03 Jun 2021
Warp Consistency for Unsupervised Learning of Dense Correspondences
Warp Consistency for Unsupervised Learning of Dense Correspondences
Prune Truong
Martin Danelljan
F. I. F. Richard Yu
Luc Van Gool
20
45
0
07 Apr 2021
P2-Net: Joint Description and Detection of Local Features for Pixel and
  Point Matching
P2-Net: Joint Description and Detection of Local Features for Pixel and Point Matching
Bing Wang
Changhao Chen
Zhaopeng Cui
Jie Qin
Chris Xiaoxuan Lu
...
Peijun Zhao
Zhenchao Dong
Fan Zhu
Niki Trigoni
Andrew Markham
3DPC
3DV
37
48
0
01 Mar 2021
Learning Accurate Dense Correspondences and When to Trust Them
Learning Accurate Dense Correspondences and When to Trust Them
Prune Truong
Martin Danelljan
Luc Van Gool
Radu Timofte
3DH
3DPC
70
128
0
05 Jan 2021
$\mathbb{X}$Resolution Correspondence Networks
X\mathbb{X}XResolution Correspondence Networks
Georgi Tinchev
Shuda Li
Kai Han
David Mitchell
R. Kouskouridas
3DV
17
6
0
17 Dec 2020
Learnable Motion Coherence for Correspondence Pruning
Learnable Motion Coherence for Correspondence Pruning
Yuan-Bin Liu
Lingjie Liu
Chu-Hsing Lin
Zhen Dong
Wenping Wang
3DV
35
47
0
30 Nov 2020
PREDATOR: Registration of 3D Point Clouds with Low Overlap
PREDATOR: Registration of 3D Point Clouds with Low Overlap
Shengyu Huang
Zan Gojcic
Mikhail (Misha) Usvyatsov
A. Wieser
Konrad Schindler
3DPC
23
468
0
25 Nov 2020
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