13.6.12 Semantic Correspondence, Semantic Alignment

Chapter Contents (Back)
Semantic Correspondence.

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


Bachard, T.[Tom], Tom, A.J.[Anju Jose], Maugey, T.[Thomas],
Semantic Alignment for Multi-Item Compression,
ICIP22(2841-2845)
IEEE DOI 2211
Video coding, Image coding, Costs, Correlation, Shape, Image databases, Semantics, Multi image compression, Semantic, Representation Learning 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

AygŁn, 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 .


Last update:Mar 21, 2023 at 18:34:39