ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2003.12059
  4. Cited By
Correspondence Networks with Adaptive Neighbourhood Consensus

Correspondence Networks with Adaptive Neighbourhood Consensus

26 March 2020
Shuda Li
Kai Han
Theo W. Costain
Henry Howard-Jenkins
V. Prisacariu
    3DV
ArXivPDFHTML

Papers citing "Correspondence Networks with Adaptive Neighbourhood Consensus"

16 / 16 papers shown
Title
Unifying Feature and Cost Aggregation with Transformers for Semantic and
  Visual Correspondence
Unifying Feature and Cost Aggregation with Transformers for Semantic and Visual Correspondence
Sung‐Jin Hong
Seokju Cho
Seungryong Kim
Stephen Lin
ViT
56
5
0
17 Mar 2024
Integrative Feature and Cost Aggregation with Transformers for Dense
  Correspondence
Integrative Feature and Cost Aggregation with Transformers for Dense Correspondence
Sunghwan Hong
Seokju Cho
Seung Wook Kim
Stephen Lin
3DV
47
4
0
19 Sep 2022
Visual correspondence-based explanations improve AI robustness and
  human-AI team accuracy
Visual correspondence-based explanations improve AI robustness and human-AI team accuracy
Giang Nguyen
Mohammad Reza Taesiri
Anh Totti Nguyen
30
42
0
26 Jul 2022
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot
  Segmentation
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation
Sunghwan Hong
Seokju Cho
Jisu Nam
Stephen Lin
Seung Wook Kim
ViT
29
123
0
22 Jul 2022
Joint Learning of Feature Extraction and Cost Aggregation for Semantic
  Correspondence
Joint Learning of Feature Extraction and Cost Aggregation for Semantic Correspondence
Jiwon Kim
Youngjo Min
Mira Kim
Seung Wook Kim
FedML
16
0
0
05 Apr 2022
Probabilistic Warp Consistency for Weakly-Supervised Semantic
  Correspondences
Probabilistic Warp Consistency for Weakly-Supervised Semantic Correspondences
Prune Truong
Martin Danelljan
Feng Yu
Luc Van Gool
29
31
0
08 Mar 2022
CATs++: Boosting Cost Aggregation with Convolutions and Transformers
CATs++: Boosting Cost Aggregation with Convolutions and Transformers
Seokju Cho
Sunghwan Hong
Seung Wook Kim
ViT
29
34
0
14 Feb 2022
Cost Aggregation Is All You Need for Few-Shot Segmentation
Cost Aggregation Is All You Need for Few-Shot Segmentation
Sunghwan Hong
Seokju Cho
Jisu Nam
Seungryong Kim
ViT
31
23
0
22 Dec 2021
DenseGAP: Graph-Structured Dense Correspondence Learning with Anchor
  Points
DenseGAP: Graph-Structured Dense Correspondence Learning with Anchor Points
Zhengfei Kuang
Jiaman Li
Mingming He
Tong Wang
Yajie Zhao
32
16
0
13 Dec 2021
The Animation Transformer: Visual Correspondence via Segment Matching
The Animation Transformer: Visual Correspondence via Segment Matching
Evan Casey
V. Pérez
Zhuoru Li
Harry Teitelman
Nick Boyajian
Tim Pulver
Mike Manh
William Grisaitis
ViT
19
30
0
06 Sep 2021
Relational Embedding for Few-Shot Classification
Relational Embedding for Few-Shot Classification
Dahyun Kang
Heeseung Kwon
Juhong Min
Minsu Cho
39
185
0
22 Aug 2021
CATs: Cost Aggregation Transformers for Visual Correspondence
CATs: Cost Aggregation Transformers for Visual Correspondence
Seokju Cho
Sunghwan Hong
Sangryul Jeon
Yunsung Lee
Kwanghoon Sohn
Seungryong Kim
ViT
26
86
0
04 Jun 2021
Hypercorrelation Squeeze for Few-Shot Segmentation
Hypercorrelation Squeeze for Few-Shot Segmentation
Juhong Min
Dahyun Kang
Minsu Cho
34
288
0
04 Apr 2021
Convolutional Hough Matching Networks
Convolutional Hough Matching Networks
Juhong Min
Minsu Cho
23
65
0
31 Mar 2021
GOCor: Bringing Globally Optimized Correspondence Volumes into Your
  Neural Network
GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network
Prune Truong
Martin Danelljan
Luc Van Gool
Radu Timofte
30
76
0
16 Sep 2020
Co-Attention for Conditioned Image Matching
Co-Attention for Conditioned Image Matching
Olivia Wiles
Sébastien Ehrhardt
Andrew Zisserman
VLM
24
13
0
16 Jul 2020
1