SIGNATURE VERIFICATION USING A “SIAMESE” TIME DELAY NEURAL NETWORK

Untitled work

(1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895

Generalization and network design strategies

LeCun, Y. (1989) · Cited by 885

Neural Networks for Fingerprint Recognition

Baldi, P., & Chauvin, Y. (1993) · Neural Computation · doi:10.1162/neco.1993.5.3.402 · Cited by 171

Automatic Signature Verification Based on Accelerometry

Herbst, N. M., & Liu, C. N. (1977) · IBM Journal of Research and Development · doi:10.1147/rd.213.0245 · Cited by 108

A time delay neural network architecture for speech recognition

Lang, K. (1989) · Cited by 76

Signature verification from position, velocity and acceleration signals: a comparative study

Réjean Plamondon, B., & Parizeau, M. (2003) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.1988.28218 · Cited by 54

DYNAMIC APPROACHES TO HANDWRITTEN SIGNATURE VERIFICATION

Lorette, G., & Réjean Plamondon, B. (1990) · WORLD SCIENTIFIC eBooks · doi:10.1142/9789814439329_0002 · Cited by 26

A time delay neural network character recognizer for a touch terminal

Guyon, I., Albrecht, P., LeCun, Y., et al. (1990) · Cited by 8

DeepPTZ: Deep Self-Calibration for PTZ Cameras

Zhang, C., Rameau, F., Kim, J., Argaw, D. M., Bazin, J., & Kweon, I. S. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093629 · Cited by 47

Robust Point Cloud Registration Framework Based on Deep Graph Matching

Fu, K., Luo, J., Luo, X., Liu, S., Zhang, C., & Wang, M. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3204713 · Cited by 51

CNN-Siam: multimodal siamese CNN-based deep learning approach for drug‒drug interaction prediction

Yang, Z., Tong, K., Jin, S., Wang, S., Yang, C., & Jiang, F. X. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05242-y · Cited by 48

Deep Convolutional Neural Networks for Multi-instance Multi-task Learning

Zeng, T., & Ji, S. (2015) · Proceedings · doi:10.1109/icdm.2015.92 · Cited by 46

Geometric deep learning

Masci, J., Rodolà, E., Boscaini, D., Bronstein, M. M., & Li, H. (2016) · SIGGRAPH Asia Courses · doi:10.1145/2988458.2988485 · Cited by 51

An Empirical Study on Writer Identification and Verification From Intra-Variable Individual Handwriting

Adak, C., Chaudhuri, B. B., & Blumenstein, M. (2019) · IEEE Access · doi:10.1109/access.2019.2899908 · Cited by 55

Mitigating long tail effect in recommendations using few shot learning technique

Sreepada, R. S., & Patra, B. K. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112887 · Cited by 49

Learning Intra-Batch Connections for Deep Metric LearningPreprint

Seidenschwarz, J., Elezi, I., & Leal-Taixé, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.07753 · Cited by 46

Few-shot and meta-learning methods for image understanding: a survey

He, K., Pu, N., Lao, M., & Lew, M. S. (2023) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-023-00279-4 · Cited by 51

Artificial Intelligence: The Future for Organic Chemistry?

Peiretti, F., & Brunel, J. M. (2018) · ACS Omega · doi:10.1021/acsomega.8b01773 · Cited by 50

Image-Based Model Parameter Optimization Using Model-Assisted Generative Adversarial Networks

Monsalve, S. A., & Whitehead, L. H. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2969327 · Cited by 48

Image Retrieval and Pattern Spotting using Siamese Neural Network

Wiggers, K. L., Britto, A. S., Heutte, L., Koerich, A. L., & Oliveira, L. S. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852197 · Cited by 47

Meta-Learning for Fast Classifier Adaptation to New Users of Signature Verification Systems

Hafemann, L. G., Sabourin, R., & Oliveira, L. S. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2949425 · Cited by 47

Deep Dynamic Time Warping: End-to-End Local Representation Learning for Online Signature Verification

Wu, X., Kimura, A., Iwana, B. K., et al. (2019) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2019.00179 · Cited by 45

Modeling Functional Similarity in Source Code With Graph-Based Siamese Networks

Mehrotra, N., Agarwal, N., Gupta, P., Anand, S., Lo, D., & Purandare, R. (2021) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2021.3105556 · Cited by 52

Estimating Demand Flexibility Using Siamese LSTM Neural Networks

Ruan, G., Kirschen, D. S., Zhong, H., Xia, Q. L., & Kang, C. (2021) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2021.3110723 · Cited by 47

Representation Learning of Music Using Artist LabelsPreprint

Park, J. Y., Lee, J., Park, J., Ha, J.-W., & Nam, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.06648 · Cited by 50

Semantic Feature Augmentation in Few-shot Learning.Preprint

Chen, Z., Fu, Y., Zhang, Y., Jiang, Y., Xue, X., & Sigal, L. (2018) · arXiv (Cornell University) · Cited by 45

Exemplar-Driven Top-Down Saliency Detection via Deep Association

He, S., & Lau, R. W. H. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.617 · Cited by 43

Leveraging siamese networks for one-shot intrusion detection model

Hindy, H., Tachtatzis, C., Atkinson, R. C., Brosset, D., Bureš, M., Andonović, I., Michie, C., & Bellekens, X. (2022) · Journal of Intelligent Information Systems · doi:10.1007/s10844-022-00747-z · Cited by 50

HAN-BSVD: A hierarchical attention network for binary software vulnerability detection

Han, Y., Luo, S., Pan, L., & Zhang, Y. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102286 · Cited by 44

KNOWLEDGE TRANSFER IN DEEP CONVOLUTIONAL NEURAL NETS

Gutstein, S., Fuentes, O., & Freudenthal, E. (2008) · International Journal of Artificial Intelligence Tools · doi:10.1142/s0218213008004059 · Cited by 44

Fine-grained Patient Similarity Measuring using Deep Metric Learning

Ni, J., Liu, J., Zhang, C., Ye, D., & Ma, Z. (2017) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3132847.3133022 · Cited by 44

VLASE: Vehicle Localization by Aggregating Semantic Edges

Yu, X., Chaturvedi, S., Feng, C., Taguchi, Y., Lee, T., Fernandes, C., & Ramalingam, S. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8594358 · Cited by 44

An Efficient Hybrid Recommendation Model With Deep Neural Networks

Huang, Z., Yu, C. Y., Juan, N., Liu, H., Zeng, C. Y., & Tang, Y. Z. (2019) · IEEE Access · doi:10.1109/access.2019.2929789 · Cited by 46

DP-Siam: Dynamic Policy Siamese Network for Robust Object Tracking

Abdelpakey, M. H., & Shehata, M. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2942506 · Cited by 43

Deep Learning in Biometrics: A Survey

López, A. B. (2019) · ADCAIJ ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL · doi:10.14201/adcaij2019841932 · Cited by 44

Anatomy-Regularized Representation Learning for Cross-Modality Medical Image Segmentation

Chen, X., Lian, C., Wang, L., Deng, H., Kuang, T., Fung, S. H., Gateño, J., Yap, P., Xia, J. J., & Shen, D. G. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3025133 · Cited by 46

Unsuccessful story about few shot malware family classification and siamese network to the rescue

Bai, Y., Xing, Z., Li, X., Feng, Z., & Ma, D. (2020) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3377811.3380354 · Cited by 44

Neural ranking models for document retrieval

Trabelsi, M., Chen, Z., Davison, B. D., & Heflin, J. (2021) · Information Retrieval · doi:10.1007/s10791-021-09398-0 · Cited by 56

Natural Language Inference by Tree-Based Convolution and Heuristic MatchingPreprint

Mou, L., Men, R., Li, G., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1512.08422 · Cited by 44

Neural Information Retrieval: A Literature ReviewPreprint

Zhang, Y., Rahman, M. M., Braylan, A., Dang, B., Chang, H.-L., Kim, H., McNamara, Q., Angert, A., Banner, E., Khetan, V., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.06792 · Cited by 76

Intra-class variations with deep learning-based gait analysis: A comprehensive survey of covariates and methods

Parashar, A., Shekhawat, R. S., Ding, W., & Rida, I. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.07.002 · Cited by 46

Person Reidentification Using Deep Convnets With Multitask Learning

McLaughlin, N., del Rincón, J. M., & Miller, P. C. (2016) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2016.2619498 · Cited by 43

Learning image-based spatial transformations via convolutional neural networks: A review

Tustison, N. J., Avants, B., & Gee, J. C. (2019) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2019.05.037 · Cited by 45

Re-Identification and growth detection of pulmonary nodules without image registration using 3D siamese neural networks

Rafael-Palou, X., Aubanell, A., Bonavita, I., Ceresa, M., Piella, G., Ribas, V. J., & Ballester, M. Á. G. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101823 · Cited by 43

Computational Intelligence in Remote Sensing Image Registration: A survey

Wu, Y., Liu, J., Zhu, C.-Z., Bai, Z., Miao, Q., Ma, W., & Gong, M. (2020) · Machine Intelligence Research · doi:10.1007/s11633-020-1248-x · Cited by 43

A Deep-Learning Sequence-Based Method to Predict Protein Stability Changes Upon Genetic Variations

Pancotti, C., Benevenuta, S., Repetto, V., Birolo, G., Capriotti, E., Sanavia, T., & Fariselli, P. (2021) · Genes · doi:10.3390/genes12060911 · Cited by 45

ContrasGAN: Unsupervised domain adaptation in Human Activity Recognition via adversarial and contrastive learning

Sanabria, A. R., Zambonelli, F., Dobson, S., & Ye, J. (2021) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2021.101477 · Cited by 43

Review of Surface-Defect Detection Methods for Industrial Products Based on Machine Vision

Wang, Q., Wang, M., Sun, J., Chen, D., & Shi, P. (2025) · IEEE Access · doi:10.1109/access.2025.3571297 · Cited by 53

A novel face presentation attack detection scheme based on multi-regional convolutional neural networks

Ma, Y., Wu, L., Li, Z., & Liu, F. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.01.002 · Cited by 43

ST-SiameseNet: Spatio-Temporal Siamese Networks for Human Mobility Signature Identification

Ren, H., Pan, M., Li, Y., Zhou, X. S., & Luo, J. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403183 · Cited by 42

H2MN

Zhang, Z., Bu, J., Ester, M., Li, Z., Yao, C., Yu, Z., & Wang, C. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467328 · Cited by 47

Cover Detection Using Dominant Melody EmbeddingsPreprint

Doras, G., & Peeters, G. G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.01824 · Cited by 41

A convolutional neural network approach for face verification

Khalil-Hani, M., & Sung, L. S. (2014) · International Conference on High Performance Computing & Simulation (HPCS) · doi:10.1109/hpcsim.2014.6903759 · Cited by 42

Joint Learning of Speaker and Phonetic Similarities with Siamese Networks

Zeghidour, N., Synnaeve, G., Usunier, N., & Emmanuel Dupoux, A. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-811 · Cited by 41

Sentiment Analysis of Code-Mixed Languages Leveraging Resource Rich Languages

Choudhary, N., Singh, R., Bindlish, I., & Shrivastava, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-23804-8_9 · Cited by 45

Learning Graph Distances with Message Passing Neural Networks

Riba, P., Fischer, A., Lladós, J., & Fornés, A. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545310 · Cited by 41

Bilateral Dependency Neural Networks for Cross-Language Algorithm Classification

Bui, N. D. Q., Yu, Y., & Jiang, L. (2019) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner.2019.8667995 · Cited by 40

Multi-scale Microaneurysms Segmentation Using Embedding Triplet Loss

Sarhan, M. H., Albarqouni, S., Yiğitsoy, M., Navab, N., & Eslami, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32239-7_20 · Cited by 41

Siamese Dense Network for Reflection Removal with Flash and No-Flash Image Pairs

Chang, Y., Jung, C., Sun, J., & Wang, F. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01276-z · Cited by 41

RankSum—An unsupervised extractive text summarization based on rank fusion

Joshi, A., Fidalgo, E., Alegre, E., & Aláiz-Rodríguez, R. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116846 · Cited by 47

Unsupervised discriminative feature learning via finding a clustering-friendly embedding space

Cao, W., Zhang, Z., Liu, C., Li, R., Jiao, Q., Yu, Z., & Wong, H. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108768 · Cited by 41

Scale-Localized Abstract Reasoning

Benny, Y., Pekar, N., & Wolf, L. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01237 · Cited by 45

ShapeNet: Convolutional Neural Networks on Non-Euclidean Manifolds

Masci, J., Boscaini, D., Bronstein, M. M., & Vandergheynst, P. (2015) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 43

Siamese Style Convolutional Neural Networks for Sound Search by Vocal Imitation

Zhang, Y., Pardo, B., & Duan, Z. (2018) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2018.2868428 · Cited by 40

Class-balanced siamese neural networks

Berlemont, S., Lefebvre, G., Duffner, S., & García, C. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.07.060 · Cited by 42

End-to-End Resume Parsing and Finding Candidates for a Job Description using BERTPreprint

Bhatia, V., Rawat, P., Kumar, A., & Shah, R. R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.03089 · Cited by 69

Siamese learning based on graph differential equation for Next-POI recommendation

Yang, Y., Zhou, S., Weng, H., Wang, D., Zhang, X., Yu, D., & Deng, S. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.111086 · Cited by 40

A plant disease classification using one-shot learning technique with field images

Saad, M. H., & Salman, A. E. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17830-4 · Cited by 39

Content-Based Representations of Audio Using Siamese Neural Networks

Manocha, P., Badlani, R., Kumar, A., Shah, A., Elizalde, B., & Raj, B. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461524 · Cited by 39

MIX

Chen, H., Han, F. X., Niu, D., Liu, D., Lai, K., Wu, C., & Xu, Y. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3219928 · Cited by 38

Learning 3D Keypoint Descriptors for Non-rigid Shape Matching

Wang, H., Guo, J., Dong-ming Yan, A., Quan, W., & Zhang, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01237-3_1 · Cited by 39

Siamese Neural Networks for User Identity Linkage Through Web Browsing

Qiao, Y., Wu, Y., Duo, F., Lin, W., & Yang, J. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2929575 · Cited by 39

A Stroke-Based RNN for Writer-Independent Online Signature Verification

Li, C., Zhang, X., Lin, F., Wang, Z., Liu, J., Zhang, R., & Wang, H. (2019) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2019.00090 · Cited by 41

DeepHSV: User-Independent Offline Signature Verification Using Two-Channel CNN

Li, C., Lin, F., Wang, Z., et al. (2019) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2019.00035 · Cited by 39

Spectrogram Classification Using Dissimilarity Space

Nanni, L., Rigo, A., Lumini, A., & Brahnam, S. (2020) · Applied Sciences · doi:10.3390/app10124176 · Cited by 40

NN-EVCLUS: Neural network-based evidential clustering

Denœux, T. (2021) · Information Sciences · doi:10.1016/j.ins.2021.05.011 · Cited by 41

Multi-Modal Siamese Network for Diagnostically Similar Lesion Retrieval in Prostate MRI

Rossi, A., Hosseinzadeh, M., Bianchini, M., Scarselli, F., & Huisman, H. J. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3043641 · Cited by 39

Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks

de Oliveira, Í. O., Laroca, R., Menotti, D., Fonseca, K. V. O., & Minetto, R. (2021) · IEEE Access · doi:10.1109/access.2021.3097964 · Cited by 48

Meta-learning spiking neural networks with surrogate gradient descent

Stewart, K. M., & Neftci, E. (2022) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/ac8828 · Cited by 39

Nonstationary data stream classification with online active learning and siamese neural networks✩

Malialis, K., Panayiotou, C. G., & Polycarpou, M. M. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.09.065 · Cited by 41

CLCD-I: Cross-Language Clone Detection by Using Deep Learning with InferCode

Yahya, M., & Kim, D. (2023) · Computers · doi:10.3390/computers12010012 · Cited by 39

P2OP—Plant Pathology on Palms: A deep learning-based mobile solution for in-field plant disease detection

Janarthan, S., Thuseethan, S., Rajasegarar, S., & Yearwood, J. L. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.107371 · Cited by 46

SDR-Former: A Siamese Dual-Resolution Transformer for liver lesion classification using 3D multi-phase imaging

Lou, M., Ying, H., Liu, X., Zhou, H.-Y., Zhang, Y., & Yu, Y. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107228 · Cited by 44

Similarity Learning for Authorship Verification in Social Media

Boenninghoff, B., Nickel, R. M., Zeiler, S., & Kolossa, D. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683405 · Cited by 46

Latent Space Subdivision: Stable and Controllable Time Predictions for Fluid Flow

Wiewel, S., Kim, B., Azevedo, V. C., et al. (2020) · Computer Graphics Forum · doi:10.1111/cgf.14097 · Cited by 38

Biomedical Data and Deep Learning Computational Models for Predicting Compound-Protein Relations

Zhao, Q., Yang, M., Cheng, Z. A., Li, Y., & Wang, J. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3069040 · Cited by 39

RLAS-BIABC: A Reinforcement Learning-Based Answer Selection Using the BERT Model Boosted by an Improved ABC Algorithm

Gharagozlou, H., Mohammadzadeh, J., Bastanfard, A., & Ghidary, S. S. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/7839840 · Cited by 41

Scalable Multi-Temporal Remote Sensing Change Data Generation via Simulating Stochastic Change Process

Zheng, Z., Tian, S., Ma, A., Zhang, L., & Zhong, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01994 · Cited by 40

Advances in deep neural network-based hyperspectral image classification and feature learning with limited samples: a survey

Ullah, F., Ullah, I., Khan, K., Khan, S., & Amin, F. (2025) · Applied Intelligence · doi:10.1007/s10489-024-06139-w · Cited by 37

Unsupervised and transfer learning challenge

Guyon, I., Dror, G., Lemaire, V., Taylor, G. W., & Aha, D. W. (2011) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2011.6033302 · Cited by 37

DIMAL: Deep Isometric Manifold Learning Using Sparse Geodesic Sampling

Pai, G., Talmon, R., Bronstein, A. M., et al. (2019) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2019.00092 · Cited by 37

Contrastively Smoothed Class Alignment for Unsupervised Domain Adaptation

Dai, S., Cheng, Y., Zhang, Y., Gan, Z., Liu, J., & Carin, L. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69538-5_17 · Cited by 37

Differential Morph Face Detection using Discriminative Wavelet Sub-bands

Chaudhary, B., Aghdaie, P., Soleymani, S., Dawson, J. M., & Nasrabadi, N. M. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00158 · Cited by 37

A deep recurrent neural network approach to learn sequence similarities for user-identification

Vamosi, S., Reutterer, T., & Platzer, M. (2022) · Decision Support Systems · doi:10.1016/j.dss.2021.113718 · Cited by 39

Modeling Time Series Similarity with Siamese Recurrent NetworksPreprint

Pei, W., Tax, D. M. J., & van der Maaten, L. J. P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.04713 · Cited by 55

DF-SLAM: A Deep-Learning Enhanced Visual SLAM System based on Deep Local FeaturesPreprint

Kang, R., Shi, J., Li, X., Liu, Y., & Liu, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.07223 · Cited by 37

Extending and Analyzing Self-supervised Learning Across Domains

Wallace, B., & Hariharan, B. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58574-7_43 · Cited by 35

Estimation of absolute states of human skeletal muscle via standard B-mode ultrasound imaging and deep convolutional neural networks

Cunningham, R. J., & Loram, I. D. (2020) · Journal of The Royal Society Interface · doi:10.1098/rsif.2019.0715 · Cited by 36

Multimodal One-shot Learning of Speech and Images

Eloff, R., Engelbrecht, H. A., & Kamper, H. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683587 · Cited by 35

Learning an Evolutionary Embedding via Massive Knowledge Distillation

Wu, X., He, R., Hu, Y., & Sun, Z. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01286-x · Cited by 34

Unsupervised Learning of Category-Specific Symmetric 3D Keypoints from Point Sets

Fernandez-Labrador, C., Chhatkuli, A., Paudel, D. P., Guerrero, J. J., Demonceaux, C., & Gool, L. V. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58595-2_33 · Cited by 36

Grale

Halcrow, J., Moșoi, A., Ruth, S., & Perozzi, B. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403302 · Cited by 42

Speech emotion recognition via learning analogies

Ntalampiras, S. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.01.018 · Cited by 42

A Siamese Vision Transformer for Bearings Fault Diagnosis

He, Q., Li, S., Bai, Q., Zhang, A., Yang, J., & Shen, M. (2022) · Micromachines · doi:10.3390/mi13101656 · Cited by 39

Learning Wasserstein EmbeddingsPreprint

Courty, N., Flamary, R., & Ducoffe, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.07457 · Cited by 36

Advances in Rapid Damage Identification Methods for Post-Disaster Regional Buildings Based on Remote Sensing Images: A Survey

Gu, J., Xie, Z., Zhang, J., et al. (2024) · Buildings · doi:10.3390/buildings14040898 · Cited by 35

Few-Shot Siamese Neural Networks Employing Audio Features for Human-Fall Detection

Droghini, D., Vesperini, F., Principi, E., Squartini, S., & Piazza, F. (2018) · International Conference on Pattern Recognition and Artificial Intelligence (PRAI) · doi:10.1145/3243250.3243268 · Cited by 35

On the use of pairwise distance learning for brain signal classification with limited observations

Calhas, D., Romero, E., & Henriques, R. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101852 · Cited by 35

DETERMINING QUALITY REQUIREMENTS AT THE UNIVERSITIES TO IMPROVE THE QUALITY OF EDUCATION

Mustafa, N. H., Kelesbayev, D., & Alibekova, Z. D. (2018) · The Journal of Economic Research & Business Administration · doi:10.26577/be-2018-2-2141

Susquehanna Chorale Spring Concert "Roots and Wings"

Hawkins, D. (2017)

Using DataGrid Control to Realize DataBase of Querying in VB6.0

Hu, S. (2000) · Modern Computer

Study and Two Types of Typical Usage of DataGrid Web Server Control

Hai-tao, L. (2005) · Modern Computer

Bidirectional Sort and Choosing a Row to Update or Delete by Click Any Cell in DataGrid

Tian-yuan, X. (2006) · Jisuanji gongcheng