Sharma, D.[Divya],
Chattopadhyay, C.[Chiranjoy],
High-level feature aggregation for fine-grained architectural floor
plan retrieval,
IET-CV(12), No. 5, August 2018, pp. 702-709.
DOI Link
1807
BibRef
Sharma, D.[Divya],
Chattopadhyay, C.[Chiranjoy],
Harit, G.,
A unified framework for semantic matching of architectural floorplans,
ICPR16(2422-2427)
IEEE DOI
1705
Databases, Feature extraction, Image segmentation, Layout, Semantics,
Solid modeling, Topology
BibRef
Ham, B.[Bumsub],
Cho, M.S.[Min-Su],
Schmid, C.[Cordelia],
Ponce, J.[Jean],
Proposal Flow: Semantic Correspondences from Object Proposals,
PAMI(40), No. 7, July 2018, pp. 1711-1725.
IEEE DOI
1806
BibRef
Earlier:
Proposal Flow,
CVPR16(3475-3484)
IEEE DOI
1612
Benchmark testing, Clutter, Optical imaging,
Proposals, Robustness, Semantics, Semantic flow,
scene alignment.
Correspondences among object.
BibRef
Xiao, T.H.[Tai-Hong],
Liu, S.F.[Si-Fei],
de Mello, S.[Shalini],
Yu, Z.D.[Zhi-Ding],
Kautz, J.[Jan],
Yang, M.H.[Ming-Hsuan],
Learning Contrastive Representation for Semantic Correspondence,
IJCV(130), No. 5, May 2022, pp. 1293-1309.
Springer DOI
2205
BibRef
And:
Correction:
IJCV(130), No. 6, June 2022, pp. 1607-1607.
Springer DOI
2207
BibRef
Yuan, W.T.[Wen-Tao],
Eckart, B.[Benjamin],
Kim, K.[Kihwan],
Jampani, V.[Varun],
Fox, D.[Dieter],
Kautz, J.[Jan],
DeepGMR: Learning Latent Gaussian Mixture Models for Registration,
ECCV20(V:733-750).
Springer DOI
2011
BibRef
Eckart, B.[Benjamin],
Kim, K.[Kihwan],
Kautz, J.[Jan],
HGMR: Hierarchical Gaussian Mixtures for Adaptive 3D Registration,
ECCV18(XV: 730-746).
Springer DOI
1810
BibRef
He, J.F.[Jian-Feng],
Zhang, T.Z.[Tian-Zhu],
Zheng, Y.[Yuhui],
Xu, M.L.[Ming-Liang],
Zhang, Y.D.[Yong-Dong],
Wu, F.[Feng],
Consistency Graph Modeling for Semantic Correspondence,
IP(30), 2021, pp. 4932-4946.
IEEE DOI
2106
Semantics, Feature extraction, Solid modeling, Clutter,
Image edge detection, Task analysis, Strain,
cycle consistency
BibRef
Jeon, S.[Sangryul],
Kim, S.[Seungryong],
Min, D.B.[Dong-Bo],
Sohn, K.H.[Kwang-Hoon],
Pyramidal Semantic Correspondence Networks,
PAMI(44), No. 12, December 2022, pp. 9102-9118.
IEEE DOI
2212
Semantics, Computer architecture, Proposals, Strain,
Feature extraction, Robustness, Microprocessors,
coarse-to-fine inference
BibRef
Liu, H.[He],
Wang, T.[Tao],
Li, Y.D.[Yi-Dong],
Lang, C.[Congyan],
Jin, Y.[Yi],
Ling, H.B.[Hai-Bin],
Joint Graph Learning and Matching for Semantic Feature Correspondence,
PR(134), 2023, pp. 109059.
Elsevier DOI
2212
Feature correspondence, Attention network, Graph matching, Graph learning
BibRef
Sachdeva, R.[Ragav],
Cordeiro, F.R.[Filipe Rolim],
Belagiannis, V.[Vasileios],
Reid, I.D.[Ian D.],
Carneiro, G.[Gustavo],
ScanMix: Learning from Severe Label Noise via Semantic Clustering and
Semi-Supervised Learning,
PR(134), 2023, pp. 109121.
Elsevier DOI
2212
Noisy label learning, Semi-supervised learning,
Semantic clustering, Self-supervised Learning, Expectation maximisation
BibRef
Xu, X.[Xianda],
Xu, X.[Xing],
Shen, F.M.[Fu-Min],
Li, Y.J.[Yu-Jie],
Semantic-Aligned Attention With Refining Feature Embedding for
Few-Shot Image Classification,
ITS(23), No. 12, December 2022, pp. 25458-25468.
IEEE DOI
2212
Semantics, Task analysis, Visualization, Training,
Feature extraction, Autonomous vehicles, Real-time systems,
visual-semantic alignment
BibRef
Yang, Z.Q.[Zai-Quan],
Zhang, Y.[Yuqi],
Du, Y.X.[Yu-Xin],
Tong, C.[Chao],
Semantic-aligned reinforced attention model for zero-shot learning,
IVC(128), 2022, pp. 104586.
Elsevier DOI
2212
Zero-shot learning, Semantic alignment, Attributes location, Attention
BibRef
Wang, J.[Jie],
Zhang, Z.Q.[Zhan-Qiu],
Shi, Z.H.[Zhi-Hao],
Cai, J.Y.[Jian-Yu],
Ji, S.W.[Shui-Wang],
Wu, F.[Feng],
Duality-Induced Regularizer for Semantic Matching Knowledge Graph
Embeddings,
PAMI(45), No. 2, February 2023, pp. 1652-1667.
IEEE DOI
2301
Semantics, Tensors, Computational modeling, Analytical models,
Predictive models, Minimization, Triples (Data structure),
temporal knowledge graphs
BibRef
Liu, W.X.[Wen-Xuan],
Zhong, X.[Xian],
Jia, X.M.[Xue-Mei],
Jiang, K.[Kui],
Lin, C.W.[Chia-Wen],
Actor-Aware Alignment Network for Action Recognition,
SPLetters(29), 2022, pp. 2597-2601.
IEEE DOI
2301
Semantics, Cognition, Surveillance, Streaming media, Strain,
Motion segmentation, Action recognition, semantic correspondence,
spatio-temporal alignment
BibRef
Wang, Z.[Zi],
Fu, Z.H.[Zhi-Heng],
Guo, Y.L.[Yu-Lan],
Li, Z.[Zhang],
Yu, Q.F.[Qi-Feng],
Local-to-Global Cost Aggregation for Semantic Correspondence,
CirSysVideo(33), No. 3, March 2023, pp. 1209-1222.
IEEE DOI
2303
Costs, Correlation, Semantics, Transformers, Task analysis,
Feature extraction, Clutter, Semantic matching, transformer
BibRef
Hu, Y.D.[Ying-Dong],
Wang, R.[Renhao],
Zhang, K.[Kaifeng],
Gao, Y.[Yang],
Semantic-Aware Fine-Grained Correspondence,
ECCV22(XXXI:97-115).
Springer DOI
2211
BibRef
Kim, J.[Jiwon],
Ryoo, K.[Kwangrok],
Seo, J.[Junyoung],
Lee, G.[Gyuseong],
Kim, D.[Daehwan],
Cho, H.[Hansang],
Kim, S.[Seungryong],
Semi-Supervised Learning of Semantic Correspondence with
Pseudo-Labels,
CVPR22(19667-19677)
IEEE DOI
2210
Training, Photography, Semantics, Supervised learning,
Predictive models, Semisupervised learning, Benchmark testing,
Self- semi- meta- unsupervised learning
BibRef
Kim, S.[Seungwook],
Min, J.[Juhong],
Cho, M.[Minsu],
TransforMatcher: Match-to-Match Attention for Semantic Correspondence,
CVPR22(8687-8697)
IEEE DOI
2210
Location awareness, Knowledge engineering, Correlation,
Image matching, Semantics, Computer architecture, Visual reasoning
BibRef
Ye, H.J.[Han-Jia],
Shi, Y.[Yi],
Zhan, D.C.[De-Chuan],
Identifying Ambiguous Similarity Conditions via Semantic Matching,
CVPR22(16589-16598)
IEEE DOI
2210
Semantics, Benchmark testing, Predictive models, Birds,
Pattern recognition, Aircraft, Representation learning,
Self- semi- meta- unsupervised learning
BibRef
Huang, S.[Shuaiyi],
Yang, L.[Luyu],
He, B.[Bo],
Zhang, S.Y.[Song-Yang],
He, X.M.[Xu-Ming],
Shrivastava, A.[Abhinav],
Learning Semantic Correspondence with Sparse Annotations,
ECCV22(XIV:267-284).
Springer DOI
2211
BibRef
Aygün, M.[Mehmet],
Aodha, O.M.[Oisin Mac],
Demystifying Unsupervised Semantic Correspondence Estimation,
ECCV22(XXX:125-142).
Springer DOI
2211
BibRef
Li, X.[Xin],
Fan, D.P.[Deng-Ping],
Yang, F.[Fan],
Luo, A.[Ao],
Cheng, H.[Hong],
Liu, Z.C.[Zi-Cheng],
Probabilistic Model Distillation for Semantic Correspondence,
CVPR21(7501-7510)
IEEE DOI
2111
WWW Link.
Code, Matching. Codes, Annotations, Semantics, Training data,
Probabilistic logic, Data models
BibRef
Zhao, D.Y.[Dong-Yang],
Song, Z.Y.[Zi-Yang],
Ji, Z.H.[Zheng-Hao],
Zhao, G.M.[Gang-Ming],
Ge, W.F.[Wei-Feng],
Yu, Y.Z.[Yi-Zhou],
Multi-scale Matching Networks for Semantic Correspondence,
ICCV21(3334-3344)
IEEE DOI
2203
WWW Link. Codes, Fuses, Semantics, Buildings, Benchmark testing,
Computational efficiency, Scene analysis and understanding
BibRef
Lee, J.Y.[Jae Yong],
de Gol, J.[Joseph],
Fragoso, V.[Victor],
Sinha, S.N.[Sudipta N.],
PatchMatch-Based Neighborhood Consensus for Semantic Correspondence,
CVPR21(13148-13158)
IEEE DOI
2111
Deep learning, Costs, Computational modeling,
Semantics, Memory management, Feature extraction
BibRef
Liu, Y.B.[Yan-Bin],
Zhu, L.C.[Lin-Chao],
Yamada, M.[Makoto],
Yang, Y.[Yi],
Semantic Correspondence as an Optimal Transport Problem,
CVPR20(4462-4471)
IEEE DOI
2008
Semantics, Correlation, Computational modeling, Clutter,
Task analysis, Optimal matching, Pattern matching
BibRef
Laskar, Z.[Zakaria],
Kannala, J.H.[Ju-Ho],
Semi-supervised Semantic Matching,
DeepLearn-G18(III:444-455).
Springer DOI
1905
BibRef
Laskar, Z.,
Melekhov, I.,
Tavakoli, H.R.,
Ylioinas, J.,
Geometric Image Correspondence Verification by Dense Pixel Matching,
WACV20(2510-2519)
IEEE DOI
2006
Image retrieval, Pipelines, Decoding, Image resolution,
Measurement, Task analysis
BibRef
Laskar, Z.[Zakaria],
Tavakoli, H.R.,
Kannala, J.H.[Ju-Ho],
Semantic Matching by Weakly Supervised 2D Point Set Registration,
WACV19(1061-1069)
IEEE DOI
1904
convolutional neural nets, image registration,
image representation, learning (artificial intelligence),
Proposals
BibRef
Lin, C.[Chuang],
Yao, H.X.[Hong-Xun],
Yu, W.[Wei],
Sun, X.S.[Xiao-Shuai],
Cycle-Consistency Based Hierarchical Dense Semantic Correspondence,
ICIP18(818-822)
IEEE DOI
1809
Semantics, Task analysis, Estimation, Image matching, Reliability,
Image segmentation, Benchmark testing, cycle-consistency
BibRef
Han, K.[Kai],
Rezende, R.S.[Rafael S.],
Ham, B.[Bumsub],
Wong, K.Y.K.[Kwan-Yee K.],
Cho, M.S.[Min-Su],
Schmid, C.[Cordelia],
Ponce, J.[Jean],
SCNet: Learning Semantic Correspondence,
ICCV17(1849-1858)
IEEE DOI
1802
Correspondences between images depicting different
instances of the same object.
convolution, image matching, learning (artificial intelligence),
neural net architecture, PASCAL VOC 2007 keypoint dataset, SCNet,
BibRef
Yang, F.[Fan],
Li, X.[Xin],
Cheng, H.[Hong],
Li, J.P.[Jian-Ping],
Chen, L.T.[Lei-Ting],
Object-Aware Dense Semantic Correspondence,
CVPR17(4151-4159)
IEEE DOI
1711
Clutter, Proposals,
Semantics, Visualization
BibRef
Bristow, H.[Hilton],
Valmadre, J.[Jack],
Lucey, S.[Simon],
Dense Semantic Correspondence Where Every Pixel is a Classifier,
ICCV15(4024-4031)
IEEE DOI
1602
similar high-level structures.
Detectors
BibRef
Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
General References for Matching .