Shaham, U.[Uri],
Lederman, R.R.[Roy R.],
Learning by coincidence:
Siamese networks and common variable learning,
PR(74), No. 1, 2018, pp. 52-63.
Elsevier DOI
1711
Similarity learning
BibRef
He, Z.[Zhi],
He, D.[Dan],
Spatial-Adaptive Siamese Residual Network for Multi-/Hyperspectral
Classification,
RS(12), No. 10, 2020, pp. xx-yy.
DOI Link
2006
BibRef
Qin, Y.,
Bruzzone, L.,
Li, B.,
Learning Discriminative Embedding for Hyperspectral Image Clustering
Based on Set-to-Set and Sample-to-Sample Distances,
GeoRS(58), No. 1, January 2020, pp. 473-485.
IEEE DOI
2001
Clustering algorithms, Clustering methods, Training,
Hyperspectral imaging, Computational modeling,
siamese network
BibRef
Mukherjee, S.[Souvick],
Prasad, S.[Saurabh],
A spatial-spectral semisupervised deep learning framework using
siamese networks and angular loss,
CVIU(194), 2020, pp. 102943.
Elsevier DOI
2005
Semi-supervised deep learning, Angular feature extraction,
Angular softmax classification, Unsupervised pre-training,
Spatial-spectral classification
BibRef
Rao, M.[Mengbin],
Tang, P.[Ping],
Zhang, Z.[Zheng],
A Developed Siamese CNN with 3D Adaptive Spatial-Spectral Pyramid
Pooling for Hyperspectral Image Classification,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link
2006
BibRef
Ghosh, S.[Souvik],
Ghosh, S.[Spandan],
Kumar, P.[Pradeep],
Scheme, E.[Erik],
Roy, P.P.[Partha Pratim],
A novel spatio-temporal Siamese network for 3D signature recognition,
PRL(144), 2021, pp. 13-20.
Elsevier DOI
2103
Siamese network, Spatio-temporal relationships, CNN, LSTM,
3D signature recognition
BibRef
Wang, S.D.[Shi-Dong],
Ren, Y.[Yi],
Parr, G.[Gerard],
Guan, Y.[Yu],
Shao, L.[Ling],
Invariant Deep Compressible Covariance Pooling for Aerial Scene
Categorization,
GeoRS(59), No. 8, August 2021, pp. 6549-6561.
IEEE DOI
2108
Tensors, Manifolds, Feature extraction, Covariance matrices,
Remote sensing, Matrix decomposition,
Stiefel manifold and aerial scene categorization
BibRef
Jiang, X.[Xiao],
Li, G.[Gang],
Zhang, X.P.[Xiao-Ping],
He, Y.[You],
A Semisupervised Siamese Network for Efficient Change Detection in
Heterogeneous Remote Sensing Images,
GeoRS(60), 2022, pp. 1-18.
IEEE DOI
2112
Remote sensing, Training, Feature extraction, Transfer learning,
Computational efficiency, Semantics, Visualization,
transfer learning
BibRef
Zhang, L.[Lei],
He, Z.W.[Zhen-Wei],
Yang, Y.[Yi],
Wang, L.[Liang],
Gao, X.B.[Xin-Bo],
Tasks Integrated Networks:
Joint Detection and Retrieval for Image Search,
PAMI(44), No. 1, January 2022, pp. 456-473.
IEEE DOI
2112
With a siamese network
Task analysis, Feature extraction, Training, Measurement, Proposals,
Search problems, Detectors, Image search, object detection,
deep learning
BibRef
Li, A.C.[Alexander C.],
Efros, A.A.[Alexei A.],
Pathak, D.[Deepak],
Understanding Collapse in Non-contrastive Siamese Representation
Learning,
ECCV22(XXXI:490-505).
Springer DOI
2211
BibRef
Wang, X.[Xiao],
Fan, H.Q.[Hao-Qi],
Tian, Y.D.[Yuan-Dong],
Kihara, D.[Daisuke],
Chen, X.L.[Xin-Lei],
On the Importance of Asymmetry for Siamese Representation Learning,
CVPR22(16549-16558)
IEEE DOI
2210
Training, Representation learning, Visualization, Schedules,
Correlation, Systematics, Object detection,
Self- semi- meta- unsupervised learning
BibRef
Du, H.[Hang],
Shi, H.L.[Hai-Lin],
Liu, Y.[Yuchi],
Wang, J.[Jun],
Lei, Z.[Zhen],
Zeng, D.[Dan],
Mei, T.[Tao],
Semi-Siamese Training for Shallow Face Learning,
ECCV20(IV:36-53).
Springer DOI
2011
BibRef
Zhang, L.C.[Li-Chao],
Gonzalez-Garcia, A.[Abel],
van de Weijer, J.[Joost],
Danelljan, M.[Martin],
Khan, F.S.[Fahad Shahbaz],
Learning the Model Update for Siamese Trackers,
ICCV19(4009-4018)
IEEE DOI
2004
convolutional neural nets, learning (artificial intelligence),
object tracking, Siamese trackers, model update, Correlation
BibRef
Roy, S.[Soumava],
Harandi, M.[Mehrtash],
Nock, R.[Richard],
Hartley, R.I.[Richard I.],
Siamese Networks: The Tale of Two Manifolds,
ICCV19(3046-3055)
IEEE DOI
2004
Siamese networks are non-linear deep models.
geometry, gradient methods, image classification,
learning (artificial intelligence), neural nets, Optimization
BibRef
Su, W.,
Hsu, C.,
Huang, Z.,
Lin, C.,
Cheung, G.,
Joint Pairwise Learning and Image Clustering Based on a Siamese CNN,
ICIP18(1992-1996)
IEEE DOI
1809
Feature extraction, Graphics processing units, Visualization,
Clustering methods, Noise measurement,
convolutional neural network
BibRef
Vetrova, V.,
Coup, S.,
Frank, E.,
Cree, M.J.,
Hidden Features: Experiments with Feature Transfer for Fine-Grained
Multi-Class and One-Class Image Categorization,
IVCNZ18(1-6)
IEEE DOI
1902
Feature extraction, Task analysis, Convolutional neural networks,
Training, Tuning, Support vector machines, Image recognition,
Siamese networks
BibRef
Feng, W.[Wu],
Liu, D.[Dong],
Fine-Grained Image Recognition from Click-Through Logs Using Deep
Siamese Network,
MMMod17(I: 127-138).
Springer DOI
1701
Need large scale labeled datasets for training.
Dog breeds.
BibRef
Vijay Kumar, B.G.,
Carneiro, G.[Gustavo],
Reid, I.D.[Ian D.],
Learning Local Image Descriptors with Deep Siamese and Triplet
Convolutional Networks by Minimizing Global Loss Functions,
CVPR16(5385-5394)
IEEE DOI
1612
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Neural Networks Applied to Specific Problems .