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
Spatial-Adaptive Siamese Residual Network for Multi-/Hyperspectral Classification,
RS(12), No. 10, 2020, pp. xx-yy.
DOI Link 2006
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
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
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
Multi-level prediction Siamese network for real-time UAV visual tracking,
IVC(103), 2020, pp. 104002.
Elsevier DOI 2011
UAV tracking, Small target, Feature fusion, Multi-level prediction BibRef
HRSiam: High-Resolution Siamese Network, Towards Space-Borne Satellite Video Tracking,
IP(30), 2021, pp. 3056-3068.
IEEE DOI 2103
Satellites, Target tracking, Spatial resolution, Object tracking, Tracking, Robustness, Adaptation models, Satellite videos, gaussian mixture model BibRef
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
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, Computer architecture, Matrix decomposition, Stiefel manifold and aerial scene categorization BibRef
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
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
van de Weijer, J.[Joost],
Khan, F.S.[Fahad Shahbaz],
Learning the Model Update for Siamese Trackers,
IEEE DOI 2004
convolutional neural nets, learning (artificial intelligence), object tracking, Siamese trackers, model update, Correlation BibRef
Hartley, R.I.[Richard I.],
Siamese Networks: The Tale of Two Manifolds,
IEEE DOI 2004
Siamese networks are non-linear deep models. geometry, gradient methods, image classification, learning (artificial intelligence), neural nets, Optimization BibRef
Joint Pairwise Learning and Image Clustering Based on a Siamese CNN,
IEEE DOI 1809
Feature extraction, Graphics processing units, Visualization, Computer architecture, Clustering methods, Noise measurement, convolutional neural network BibRef
Hidden Features: Experiments with Feature Transfer for Fine-Grained Multi-Class and One-Class Image Categorization,
IEEE DOI 1902
Feature extraction, Task analysis, Convolutional neural networks, Training, Tuning, Support vector machines, Image recognition, Siamese networks BibRef
Fine-Grained Image Recognition from Click-Through Logs Using Deep Siamese Network,
Springer DOI 1701
Need large scale labeled datasets for training. Dog breeds. BibRef
Vijay Kumar, B.G.,
Reid, I.D.[Ian D.],
Learning Local Image Descriptors with Deep Siamese and Triplet Convolutional Networks by Minimizing Global Loss Functions,
IEEE DOI 1612
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Neural Networks Applied to Specific Problems .