Deep learning from temporal coherence in video

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SIGNATURE VERIFICATION USING A “SIAMESE” TIME DELAY NEURAL NETWORK

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Signature Verification Using A "Siamese" Time Delay Neural Network

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Multiple graph regularized graph transduction via greedy gradient Max-Cut

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Predicting Facial Attributes in Video Using Temporal Coherence and Motion-Attention

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Bi-calibration Networks for Weakly-Supervised Video Representation Learning

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"Mental Rotation" by Optimizing Transforming DistancePreprint

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A Review on Self-Supervised Learning in Time Series Anomaly Detection: Recent Advances and Open Challenges

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T-copula and Wasserstein distance-based stochastic neighbor embedding

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Pattern recognition in multilinear space and its applications: mathematics, computational algorithms and numerical validations

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Detecting Social Signals in User-Shared Images for Connection Discovery Using Deep Learning

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Recognition of Social Touch Gestures Using 3D Convolutional Neural Networks

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Self-supervised Segmentation by Grouping Optical-Flow

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DuTriNet: Dual-Stream Triplet Siamese Network for Self-Supervised Action Recognition by Modeling Temporal Correlations

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Modeling video-based anomaly detection using deep architectures: Challenges and possibilities

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Learning invariant object recognition from temporal correlation in a hierarchical network

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Generalized correntropy induced loss function for deep learning

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Cross-modal Non-linear Guided Attention and Temporal Coherence in Multi-modal Deep Video Models

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Deep Machine Learning with Spatio-Temporal Inference

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Compressed auto-encoder building block for deep learning network

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Towards efficient image and video style transfer via distillation and learnable feature transformation

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Human Motion Recognition Based on Improved 3-Dimensional Convolutional Neural Network

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Self-supervised video representation learning by maximizing mutual information

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Temporal Contrastive-Loss for Audio Event Detection

Kothinti, S. R., & Elhilali, M. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747468 · Cited by 7

Discovering New Socio-demographic Regional Patterns in Cities

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Analysis of Facial Expressiveness Captured in Reaction to Videos

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Non-reference image quality assessment based on deep clustering

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Feature fusion for human action recognition based on classical descriptors and 3D convolutional networks

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Gradient-based Training of Slow Feature Analysis by Differentiable Approximate WhiteningPreprint

Schüler, M., Hlynsson, H. D., & Wiskott, L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.08833 · Cited by 8

Unsupervised Learning of Spatiotemporally Coherent MetricsPreprint

Goroshin, R., Bruna, J., Tompson, J., Eigen, D., & LeCun, Y. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.6056 · Cited by 12

Improved Generalization of Heading Direction Estimation for Aerial Filming Using Semi-Supervised Regression

Wang, W., Ahuja, A., Zhang, Y., Bonatti, R., & Scherer, S. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793994 · Cited by 6

The Search for Equations – Learning to Identify Similarities Between Mathematical Expressions

Pfahler, L., Schill, J., & Morik, K. J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-46133-1_42 · Cited by 6

Performance Analysis and Application of Expressiveness Detection on Facial Expression Videos Using Deep Learning Techniques

Srinivasa, K. G., & Anupindi, S. (2018) · Data-Enabled Discovery and Applications · doi:10.1007/s41688-018-0018-1 · Cited by 6

Representation learning from videos in-the-wild: An object-centric approach

Romijnders, R., Mahendran, A., Tschannen, M., Djolonga, J., Ritter, M., Houlsby, N., & Lučić, M. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00022 · Cited by 6

Action Anticipation with RBF Kernelized Feature Mapping RNNPreprint

Shi, Y., Fernando, B., & Hartley, R. I. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.07806 · Cited by 6

Information theory-guided heuristic progressive multi-view coding

Li, J., Gao, H., Qiang, W., & Zheng, C. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.08.027 · Cited by 5

DeepImSeq: Deep image sequencing for unsynchronized cameras

Kanojia, G., & Raman, S. (2018) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.11.014 · Cited by 5

Exploiting Motion Information from Unlabeled Videos for Static Image Action Recognition

Zhang, Y., Niu, L., Pan, Z., Luo, M., Zhang, J., Cheng, D., & Zhang, L. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6990 · Cited by 6

Multiple Self-attention Network for Intracranial Vessel Segmentation

Li, Y., Ni, J., Elazab, A., & Wu, J. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9534214 · Cited by 5

Image concept detection in imbalanced datasets with ensemble of convolutional neural networks

Bahrami, M., & Sajedi, H. (2019) · Intelligent Data Analysis · doi:10.3233/ida-184327 · Cited by 5

Integrating Deep Learning with Correlation-based Multimedia Semantic Concept Detection

Ha, H.-Y. (2015) · doi:10.25148/etd.fidc000162 · Cited by 5

Cross-modal Learning for Multi-modal Video CategorizationPreprint

Goyal, P., Sahu, S., Ghosh, S., & Lee, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.03501 · Cited by 5

Unsupervised Activity Segmentation by Joint Representation Learning and Online Clustering.Preprint

Kumar, S., Haresh, S., Ahmed, A., Konin, A., Zia, M. Z., & Tran, Q.-H. (2021) · arXiv (Cornell University) · Cited by 5

Predicting hotel booking cancellations using tree-based neural network

Yang, D., & Miao, X. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2473 · Cited by 10

Image classification using appearance based features

Masri, D., Aung, Z., & Woon, W. L. (2015) · International Conference on Innovations in Information Technology · doi:10.1109/innovations.2015.7381527 · Cited by 4

Autoencoding slow representations for semi-supervised data-efficient regression

Struckmeier, O., Tiwari, K., & Kyrki, V. (2023) · Machine Learning · doi:10.1007/s10994-022-06299-1 · Cited by 4

Distorting Face Representations in Newborn Brains

Wood, S. M. W., & Wood, J. N. (2021) · Cognitive Science · doi:10.1111/cogs.13021 · Cited by 5

HDRVideo-GAN

Anand, M., Harilal, N., Kumar, C., & Raman, S. (2021) · Indian Conference on Computer Vision, Graphics and Image Processing · doi:10.1145/3490035.3490266 · Cited by 4

Low-Dimensional Tensor Principle Component Analysis

Itoh, H., Imiya, A., & Sakai, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-23192-1_60 · Cited by 4

Image Representation Learning Using Graph Regularized Auto-EncodersPreprint

Liao, Y., Wang, Y., & Liu, Y. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1312.0786 · Cited by 4

Deep Learning For Sequential Pattern Recognition

Safari, P. (2013) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 4

Auto-pooling: Learning to Improve Invariance of Image Features from Image SequencesPreprint

Sukhbaatar, S., Makino, T., & Aihara, K. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.3323 · Cited by 5

Self-Supervised Video Representation Learning With Odd-One-Out NetworksPreprint

Fernando, B., Bilen, H., Gavves, E., & Gould, S. J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.06646 · Cited by 11

Leveraging Time Irreversibility with Order-Contrastive Pre-trainingPreprint

Agrawal, M., Lang, H., Offin, M. D., Gazit, L., & Sontag, D. A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.02599 · Cited by 4

Mutual information guided 3D ResNet for self‐supervised video representation learning

Xue, F., Ji, H., & Zhang, W. (2020) · IET Image Processing · doi:10.1049/iet-ipr.2020.0019 · Cited by 3

Learning by Restoring Broken 3D Geometry

Liu, J., Ni, B., Chen, Y., Yu, Z., & Wang, H. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3263867 · Cited by 3

Self-Supervised Video Representation Learning via Capturing Semantic Changes Indicated by Saccades

Lai, Q., Zeng, A., Wang, Y., Cao, L., Yu, L. C., & Xu, Q. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3290938 · Cited by 3

Self-Supervised Learning of Color Constancy

Ernst, M., López, F. M., Aubret, A., Fleming, R. W., & Triesch, J. (2024) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/icdl61372.2024.10644375 · Cited by 3

Scanned to Digital Face Images Matching With Siamese Network

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Speeding up inference on deep neural networks for object detection by performing partial convolution

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Frequency–based slow feature analysis

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Modeling Long-Term Dependencies from Videos Using Deep Multiplicative Neural Networks

Si, W., Liu, C., Bi, Z., & Shan, M. (2020) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3357797 · Cited by 3

Extraction in Digital Forensic Investigation based on Video Enhancement and Machine Learning

Ponsam, J. G., Gracia, S. V. J. B., Geetha, G., Thenmozhi, M., & Nimala, K. (2021) · International Conference on Internet of Everything, Microwave Engineering, Communication and Networks (IEMECON) · doi:10.1109/iemecon53809.2021.9689110 · Cited by 3

TextTopicNet - Self-Supervised Learning of Visual Features Through Embedding Images on Semantic Text SpacesPreprint

Patel, Y., Gómez, L., Gómez, R., Rusiñol, M., Karatzas, D., & Jawahar, C. V. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.02110 · Cited by 6

Learning image representations tied to ego-motionPreprint

Jayaraman, D., & Grauman, K. L. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.02206 · Cited by 6

HDRVideo-GAN: Deep Generative HDR Video ReconstructionPreprint

Anand, M., Harilal, N., Kumar, C., & Raman, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.11795 · Cited by 3

Leukemia Classification using Deep Belief Network

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Exploring a Mixed Representation for Encoding Temporal Coherence

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Learning by Aligning 2D Skeleton Sequences and Multi-modality Fusion

Tran, Q.-H., Ahmed, M., Popattia, M., Ahmed, M. H., Konin, A., & Zia, M. Z. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72973-7_9 · Cited by 2

Robust block tensor PCA with F-norm projection framework

Zhang, X., Wang, X., Liu, Z., & Chen, J. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112712 · Cited by 2

Training Deep Autoencoder via VLC-Genetic Algorithm

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Multi-layer Joint Classification-Metric Deep Learning for Top View Image Person Re-identification

Jiao, S., Wang, J., Pan, Z., Hu, G., Zou, J., & Zeng, M. (2019) · IEEE International Conference on Electronics and Communication Engineering (ICECE) · doi:10.1109/icece48499.2019.9058499 · Cited by 2

A Self-supervised Learning System for Object Detection in Videos Using Random Walks on Graphs

Tan, J., Song, C., & Boularias, A. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561271 · Cited by 2

Robust Video Synchronization using Unsupervised Deep Learning.Preprint

Freeman, I., Wieschollek, P., & Lensch, H. P. A. (2016) · arXiv (Cornell University) · Cited by 2

Siamese neural networks for biometric hashing

Azadmanesh, M. (2014) · Sabanci University · Cited by 2

UniDual: A Unified Model for Image and Video UnderstandingPreprint

Wang, Y., Tran, D., & Torresani, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.03857 · Cited by 2

Learning Temporally-Consistent Representations for Data-Efficient Reinforcement LearningPreprint

McInroe, T., Schäfer, L., & Albrecht, S. V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.04935 · Cited by 11

Mask-Guided Self-Supervised Video Object Segmentation

Quan, R., Li, L., Yang, Z., Wang, W., & Yang, Y. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3692914 · Cited by 1

Temporal context in object recognition

Chalasani, R., & Príncipe, J. C. (2012) · Machine learning for signal processing ... · doi:10.1109/mlsp.2012.6349758 · Cited by 1

Lightweight target classification for wireless multimedia sensor networks

Mowafi, M. Y., & Aljoby, W. (2013) · International Journal of Image and Data Fusion · doi:10.1080/19479832.2013.829128 · Cited by 1

Improving Visual Feature Representations by Biasing Restricted Boltzmann Machines with Gaussian Filters

Yogeswaran, A., & Payeur, P. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-50835-1_74 · Cited by 1

Joint object and action recognition via fusion of partially observable surveillance imagery data

Shirkhodaie, A., & Chan, A. L. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2266224 · Cited by 1

Intelligent Robotic System for Solving Dissection Puzzle Combining K-Nearest Neighbors, Decision Tree and Deep Q Network

Müller, R. H., Kanso, A. M., & Xu, X. (2021) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case49439.2021.9551470 · Cited by 1

Visual Features and Their Own Optical Flow

Betti, A., Boccignone, G., Faggi, L., Gori, M., & Melacci, S. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.768516 · Cited by 1

Case-Based Inverse Reinforcement Learning Using Temporal Coherence

Nüßlein, J., Illium, S., Müller, R., Gabor, T., & Linnhoff–Popien, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-14923-8_20 · Cited by 1

Comparing Traditional Rendering Techniques to Deep Learning Based Super-Resolution in Fire and Smoke Animations

Suta, A., & Hlavacs, H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-23473-6_16 · Cited by 1

Leaping Into Memories: Space-Time Deep Feature Synthesis

Stergiou, A., & Deligiannis, N. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00188 · Cited by 1

A New Behavior Recognition Method of Nursing-Care Robots for Elderly People

Zhang, X., Liu, H., & Zhang, M. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-65978-7_82 · Cited by 1

Incorporating Scalability in Unsupervised Spatio- Temporal Feature Learning

Paul, S., Roy, S., & Roy–Chowdhury, A. K. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461758 · Cited by 1

Trading robust representations for sample complexity through self-supervised visual experience

Tacchetti, A., Voinea, S., & Evangelopoulos, G. (2018) · neural information processing systems · Cited by 1

Group k-Sparse Temporal Convolutional Neural Networks: Unsupervised Pretraining for Video Classification

Milacski, Z. Á., Póczos, B., & Lörincz, A. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852057 · Cited by 1

Self-Supervised Learning of Class Embeddings from Video

Wiles, O., Koepke, A. S., & Zisserman, A. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00364 · Cited by 1

Unsupervised Learning of Slow Features for Data Efficient Regression

Struckmeier, O., Tiwari, K., & Kyrki, V. (2021) · Cited by 1

Deep unsupervised learning from speech

Drexler, J. (2016) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 2

Ambient Sound Provides Supervision for Visual LearningPreprint

Owens, A., Wu, J., McDermott, J. H., Freeman, W. T., & Torralba, A. B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.07017 · Cited by 1

Exploiting Temporal Coherence for Multi-modal Video CategorizationPreprint

Goyal, P., Sahu, S., Ghosh, S., & Lee, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.03844 · Cited by 1

Enhancing Transformer for Video Understanding Using Gated Multi-Level Attention and Temporal Adversarial TrainingPreprint

Sahu, S., & Goyal, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.10043 · Cited by 1

Triangular similarity metric learning : A siamese architecture approach

Zheng, L. (2016) · theses.fr (ABES) · doi:10.70675/80580525z8dc6z4240za320z94b679a5476f · Cited by 1

SPATIOTEMPORAL PATTERN DETECTION IN MULTI-CELL RECORDINGS USING UNSUPERVISED LEARNING

Nikoo, H. (2015) · DalSpace (Dalhousie University)

Foundations towards an integrated theory of intelligence

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Hierarchical Dynamical Systems

Sequeira, P. M. (2015) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · doi:10.34626/knc4-p244

Unsupervised learning from videos using temporal coherency deep networks

Redondo-Cabrera, C., & López-Sastre, R. J. (2018) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2018.08.003 · Cited by 18

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Goroshin, R., Bruna, J., Tompson, J., Szlam, A., Eigen, D., & Lecun, Y. (2015)

Object-Centric Representation Learning from Unlabeled Videos

Gao, R., Jayaraman, D., & Grauman, K. L. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54193-8_16 · Cited by 30

Learning Temporal Coherent Features through Life-Time Sparsity

Springenberg, J. T., & Riedmiller, M. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34475-6_42 · Cited by 4

Unsupervised Learning of Spatiotemporally Coherent Metrics

Goroshin, R., Bruna, J., Tompson, J., Eigen, D., & LeCun, Y. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.465 · Cited by 163