Unsupervised Learning of Visual Representations Using Videos

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

Distinctive Image Features from Scale-Invariant Keypoints

Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589

Histograms of Oriented Gradients for Human Detection

Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684

Reducing the dimensionality of data with neural networks

Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928

The pascal visual object classes (VOC) challenge

Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599

SURF: Speeded Up Robust Features

Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955

Representation Learning: A Review and New Perspectives

Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001

Caffe: Convolutional Architecture for Fast Feature Embedding

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077

Extracting and composing robust features with denoising autoencoders

Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390294 · Cited by 7,436

Deep supervised hashing with hard example pairs optimization for image retrieval

Su, H., Han, M., Liang, J., Liang, J., & Yu, S. (2022) · The Visual Computer · doi:10.1007/s00371-022-02668-y · 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

Bootstrapped Self-Supervised Training with Monocular Video for Semantic Segmentation and Depth Estimation

Zhang, Y., & Leonard, J. J. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636330 · Cited by 5

Fast and Unsupervised Neural Architecture Evolution for Visual Representation Learning

Song, X., Chen, H., Xie, C., Zhang, B., Gong, X., & Doermann, D. (2021) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2021.3084394 · Cited by 5

Few-Shot Multi-Agent Perception With Ranking-Based Feature Learning

Fan, C., Hu, J., & Huang, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3285755 · Cited by 5

Structure‐Aware Image Translation‐Based Long Future Prediction for Enhancement of Ground Robotic Vehicle Teleoperation

Moniruzzaman, M., Rassau, A., Chai, D., & Islam, S. M. S. (2023) · Advanced Intelligent Systems · doi:10.1002/aisy.202200439 · Cited by 5

MGM-AE: Self-Supervised Learning on 3D Shape Using Mesh Graph Masked Autoencoders

Yang, Z., Ding, K., Liu, H., & Wang, Y. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00327 · Cited by 5

OCEAN: Object-centric arranging network for self-supervised visual representations learning

Oh, C., Ham, B., Kim, H., Hilton, A., & Sohn, K. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.01.073 · Cited by 5

Quadruplet Selection Methods for Deep Embedding Learning

Karaman, K., Gündoğdu, E., Koç, A., & Alatan, A. A. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803401 · Cited by 5

State Representation Learning For Effective Deep Reinforcement Learning

Zhao, J., Zhou, W., Zhao, T., Zhou, Y., & Li, H. (2020) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme46284.2020.9102924 · Cited by 5

Tracking Emerges by Looking Around Static Scenes, with Neural 3D Mapping

Harley, A. W., Lakshmikanth, S. K., Schydlo, P., & Fragkiadaki, K. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58574-7_36 · Cited by 5

Efficient 3D Video Engine Using Frame Redundancy

Gao, P., Pang, B., & Lu, C. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00384 · Cited by 5

What My Motion tells me about Your Pose: A Self-Supervised Monocular 3D Vehicle Detector

Picron, C., Chakravarty, P., Roussel, T., & Tuytelaars, T. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9562086 · Cited by 5

From Images to 3D Shape Attributes

Fouhey, D. F., Gupta, A., & Zisserman, A. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2782810 · Cited by 5

Communication-Efficient Distributed Deep Metric Learning with Hybrid Synchronization

Su, Y., Lyu, M. R.-T., & King, I. (2018) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3269206.3271807 · Cited by 5

Cascaded Convolutional Neural Network for Image Super-Resolution

Zhang, J., Wang, Z., Zheng, Y., & Zhang, G. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-78615-1_32 · Cited by 5

Spatio-Temporal Self-Supervision Enhanced Transformer Networks for Action Recognition

Zhang, Y., Zhang, H., Wu, G., & Li, J. (2022) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme52920.2022.9859741 · Cited by 5

Deep Reinforcement Learning in Serious Games: Analysis and Design of Deep Neural Network Architectures

Dobrovsky, A., Wilczak, C. W., Hahn, P., Hofmann, M. A., & Borghoff, U. M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-74727-9_37 · Cited by 5

SampleAhead: Online Classifier-Sampler Communication for Learning from Synthesized DataPreprint

Chen, Q., Qiu, W., Zhang, Y., Xie, L., & Yuille, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.00248 · Cited by 5

Deep Co-Clustering for Unsupervised Audiovisual Learning.Preprint

Hu, D., Nie, F., & Li, X. (2018) · arXiv (Cornell University) · 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

Multi-Face: Self-supervised Multiview Adaptation for Robust Face Clustering in VideosPreprint

Somandepalli, K., Hebbar, R., & Narayanan, S. S. (2020) · arXiv (Cornell University) · Cited by 5

Back to the Future: Cycle Encoding Prediction for Self-supervised Contrastive Video Representation LearningPreprint

Yang, X., Mirmehdi, M., & Burghardt, T. (2020) · Bristol Research (University of Bristol) · doi:10.48550/arxiv.2010.07217 · Cited by 5

Towards localisation of keywords in speech using weak supervisionPreprint

Olaleye, K., van Niekerk, B., & Kamper, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.07396 · Cited by 5

Cross-Modal Attention Consistency for Video-Audio Unsupervised LearningPreprint

Min, S., Dai, Q., Xie, H., Gan, C., Zhang, Y., & Wang, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.06939 · Cited by 5

Masked Motion Prediction with Semantic Contrast for Point Cloud Sequence Learning

Han, Y., Xu, C., Rui, X., Qian, J., & Xie, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73116-7_24 · Cited by 4

Interactive Image Retrieval Meets Query Rewriting with Large Language and Vision Language Models

Zhu, H., Huang, J.-H., Shen, Y., Rudinac, S., & Kanoulas, E. (2025) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3744910 · Cited by 4

Temporal DINO: A Self-supervised Video Strategy to Enhance Action Prediction

Teeti, I., Rongali, S. B., Singh, V. K., Bradley, A., Banerjee, B., & Fabio Cuzzolin, A. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00352 · Cited by 4

Generality-aware self-supervised transformer for multivariate time series anomaly detection

Cho, Y., Lee, J.-H., Ham, G., Jang, D., & Kim, D. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06481-7 · Cited by 4

Self-Supervised Masked Deep Embeddings for Dental Caries Detection

Almalki, A., Almalki, A., & Latecki, L. J. (2025) · IEEE Access · doi:10.1109/access.2025.3606811 · Cited by 4

SFGNet detecting objects via spatial fine-grained feature and enhanced RPN with spatial context

da Hu, J., Wang, Y., Cheng, S., Liu, J., Kang, J., & Yang, W. (2022) · Systems Science & Control Engineering · doi:10.1080/21642583.2022.2062479 · Cited by 4

EviD-GAN: Improving GAN With an Infinite Set of Discriminators at Negligible Cost

Gnanha, A. T., Cao, W., Mao, X., Wu, S., Wong, H., & Li, Q. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3388197 · Cited by 4

Bootstrap Masked Visual Modeling via Hard Patch Mining

Wang, H., Fan, J., Wang, Y., Song, K., Wang, T., Zhang, X., & Zhang, Z. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3557001 · Cited by 9

Self-supervised learning framework for efficient classification of endoscopic images using pretext tasks

Nezhad, S. A., Tajeddin, G., Khatibi, T., & Sohrabi, M. (2025) · PLoS ONE · doi:10.1371/journal.pone.0322028 · Cited by 4

Adaptive Hard Example Mining for Image Captioning

Wang, Y., Shen, Y., Xiong, H., & Lin, W. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803418 · Cited by 4

Feature‐Enhanced Occlusion Perception Object Detection for Smart Cities

Xu, J., Wang, H., Xu, M., Yang, F., Zhou, Y., & Yang, X. (2021) · Wireless Communications and Mobile Computing · doi:10.1155/2021/5544194 · Cited by 4

Mobile-Based 3D Modeling: An In-Depth Evaluation for the Application in Indoor Scenarios

Pellegrini, M. D., Orlandi, L., Sevegnani, D., & Conci, N. (2021) · Journal of Imaging · doi:10.3390/jimaging7090167 · Cited by 4

Self-Supervised Convolutional Neural Networks for Fast Gesture Recognition in Human-Robot Interaction

Patrona, F., Mademlis, I., & Pitas, I. (2021) · International Conference on Information and Automation for Sustainability (ICIAfS) · doi:10.1109/iciafs52090.2021.9605915 · Cited by 4

Driver Intent-Based Intersection Autonomous Driving Collision Avoidance Reinforcement Learning Algorithm

Chen, T., Chen, Y., Li, H., Gao, T., Tu, H., & Li, S. (2022) · Sensors · doi:10.3390/s22249943 · Cited by 4

SiamSampler: Video-Guided Sampling for Siamese Visual Tracking

Li, P., Chen, B., Bai, L., Qiao, L., Li, B., & Ouyang, W. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3214480 · Cited by 5

FUSSL: Fuzzy Uncertain Self Supervised Learning

Mohamadi, S., Doretto, G., & Adjeroh, D. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00282 · Cited by 4

A Novel Similarity Based Unsupervised Technique for Training Convolutional Filters

Erkoç, T., & Eskil, M. T. (2023) · IEEE Access · doi:10.1109/access.2023.3277253 · Cited by 4

HUCVL at MediaEval 2016: Predicting Interesting Key Frames with Deep Models.

Erdoğan, G., Erdem, A., & Erdem, E. (2016) · MediaEval · Cited by 4

ELMAENet: A Simple, Effective and Fast Deep Architecture for Image Classification

Chang, P., Zhang, J., Wang, J., & Fei, R. (2019) · Neural Processing Letters · doi:10.1007/s11063-019-10079-9 · Cited by 4

Incremental Learning Techniques Within a Self-updating Approach for Face Verification in Video-Surveillance

López‐López, E., Regueiro, C. V., Pardo, X. M., Franco, A., & Lumini, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-31321-0_3 · Cited by 4

Finding hard faces with better proposals and classifier

Zeng, X., Peng, X., Wang, Y., & Qiao, Y. (2020) · Machine Vision and Applications · doi:10.1007/s00138-020-01110-4 · Cited by 4

Addressing Class Imbalance in Scene Graph Parsing by Learning to Contrast and Score

Huang, H., Saito, S., Kikuchi, Y., Matsumoto, E., Tang, W., & Yu, P. S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69544-6_28 · Cited by 4

Unsupervised Learning from Videos for Object Discovery in Single Images

Zhao, D., Ding, B., Wu, Y., Chen, L., & Zhou, H. (2020) · Symmetry · doi:10.3390/sym13010038 · Cited by 4

Deep convolutional Triplet network for quantitative medical image analysis with comparative case study of gamma image classification

Thammasorn, P., Chaovalitwongse, W. A., Wootton, L. S., Ford, E., & Nyflot, M. J. (2017) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2017.8217813 · Cited by 4

Self-directed Lifelong Learning for Robot Vision

Schmidt, T., & Fox, D. (2019) · Springer proceedings in advanced robotics · doi:10.1007/978-3-030-28619-4_14 · Cited by 4

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

Unsupervised Learning of Semantic Audio RepresentationsPreprint

Jansen, A., Plakal, M., Pandya, R., Ellis, D. P. W., Hershey, S., Liu, J., Moore, R. C., & Saurous, R. A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.02209 · Cited by 4

Learning Deep Representations Using Convolutional Auto-encoders with Symmetric Skip ConnectionsPreprint

Dong, J., Mao, X.-J., Shen, C., & Yang, Y.-B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.09119 · Cited by 4

Ranked List Loss for Deep Metric LearningPreprint

Wang, X., Yang, H., Kodirov, E., & Robertson, N. M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.03238 · Cited by 4

Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey and Experimental StudyPreprint

Gao, B., Pan, Y., Li, C., Geng, S., & Zhao, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.04307 · Cited by 4

Self-Supervised Representation Learning for RGB-D Salient Object Detection.Preprint

Zhao, X., Pang, Y., Zhang, L., Lu, H., & Ruan, X. (2021) · arXiv (Cornell University) · Cited by 4

CTPR: Contrastive transition predictive representation for reinforcement learning

Sun, H., & Wang, C. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111124 · Cited by 4

Contrastive Visual Representation Learning Enhanced with Knowledge Embedding for Reinforcement Learning

Ma, Z. (2021) · 2021 2nd International Conference on Electronics, Communications and Information Technology (CECIT) · doi:10.1109/cecit53797.2021.00079 · Cited by 3

3D-Augmented Contrastive Knowledge Distillation for Image-based Object Pose Estimation

Liu, Z., Xing, Z., Zhou, X., Chen, Y., & Zhou, G. (2022) · International Conference on Multimedia Retrieval · doi:10.1145/3512527.3531359 · Cited by 3

A Survey of CNN-Based Techniques for Scene Flow Estimation

Muthu, S., Tennakoon, R., Hoseinnezhad, R., & Bab‐Hadiashar, A. (2023) · IEEE Access · doi:10.1109/access.2023.3314188 · Cited by 3

Text Attribute Control via Closed-Loop Disentanglement

Sha, L., & Lukasiewicz, T. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00640 · Cited by 3

How Does Contrastive Learning Organize Images?

Zhang, Y., Lu, Y., & Xuan, Q. (2024) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw60836.2024.00058 · Cited by 3

Calculating Depth for Dark Images Using Self-Supervised Learning: An Investigation of the Role of Edge Maps as Input

Dubey, S., Watts, S., Malhotra, A. K., Prajapati, K., & Chakraborty, P. (2025) · IEEE Access · doi:10.1109/access.2025.3556610 · Cited by 3

Adaptive Deep Metric Learning with harmonized loss and nearest proxy alignment for low-dimensional representation

Chen, J., Cheng, J., Wang, X., Xu, X., & Nie, F. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113631 · Cited by 3

An Entropy Clustering Approach for Assessing Visual Question Difficulty

Terao, K., Tamaki, T., Raytchev, B., Kaneda, K., & Satoh, S. (2020) · IEEE Access · doi:10.1109/access.2020.3022063 · Cited by 3

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

Cycle Fusion Network for Multi-Person Pose Estimation

Wang, Y., & Wang, T. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1550/3/032055 · Cited by 3

Learning Positional Priors for Pretraining 2D Pose Estimators

Zhang, K., Yao, P., Wu, R., Yang, C., Li, D., Du, M., Deng, K., Liu, R., & Zheng, T. (2021) · International Workshop on Human-centric Multimedia Analysis (HCMA) · doi:10.1145/3475723.3484252 · Cited by 3

Deep Learning-Based Early Detection and Avoidance of Traffic Congestion in Software-Defined Networks

Prabhavat, S., Thongthavorn, T., & Pasupa, K. (2022) · doi:10.1109/icitee56407.2022.9954107 · Cited by 5

Transformer-based Siamese and Triplet Networks for Facial Expression Intensity Estimation

Sabri, M. (2022) · International Journal of Affective Engineering · doi:10.5057/ijae.ijae-d-22-00011 · 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

Learning State Transition Rules from High-Dimensional Time Series Data with Recurrent Temporal Gaussian-Bernoulli Restricted Boltzmann Machines

Watanabe, K., & Inoue, K. (2023) · Human-Centric Intelligent Systems · doi:10.1007/s44230-023-00026-2 · 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

Label Aggregation with Self-Supervision Enhanced Graph Transformer

Liu, J., Tang, F., & Hou, X. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230431 · Cited by 3

Contrastive disentanglement for self-supervised motion style transfer

Wu, Z., Mao, S., Zhang, C., Wang, Y., & Zeng, M. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18238-4 · Cited by 3

Contrastive Unsupervised Representation Learning With Optimize-Selected Training Samples

Cheng, Y., Zhang, Z., Li, X., & Wang, S. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3424331 · Cited by 3

Fast Fashion Guided Clothing Image Retrieval: Delving Deeper into What Feature Makes Fashion

He, Y., & Chen, L. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54193-8_9 · Cited by 3

CUImage: A Neverending Learning Platform on a Convolutional Knowledge Graph of Billion Web Images

Peng, Z., Wu, L., Ren, J., Zhang, R., & Luo, P. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622090 · Cited by 3

The Study of Handwriting Recognition Algorithms Based on Neural Networks

Finkelstein, B., & Kuncan, K. (2021) · International Journal of Hybrid Information Technology · doi:10.21742/ijhit.2021.14.1.05 · Cited by 3

Re-entry Prediction for Online Conversations via Self-Supervised Learning

Wang, L., Zeng, X., Huang, H., Wong, K., & Jiang, D. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.183 · Cited by 4

Clustering-Based Representation Learning through Output Translation and Its Application to Remote-Sensing Images

Li, Q., Li, B., Garibaldi, J. M., & Qiu, G. (2022) · Remote Sensing · doi:10.3390/rs14143361 · Cited by 3

Recursive autoconvolution for unsupervised learning of convolutional neural networks

Knyazev, B. A., Barth, E., & Martinetz, T. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966158 · Cited by 3

Learning deep representations with diode loss for quantization-based similarity search

Liu, S., & Lu, H. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966132 · Cited by 3

Frequency–based slow feature analysis

Doumanoglou, A., Vretos, N., & Daras, P. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.08.067 · Cited by 3

Unsupervised Learning of ALS Point Clouds for 3-D Terrain Scene Clustering

Zhang, J., Hu, X., & Dai, H. (2021) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2020.3047096 · Cited by 3

A Sensorimotor Perspective on Contrastive Multiview Visual Representation Learning

Laflaquière, A. (2021) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2021.3086267 · Cited by 3

Disentangling Motion, Foreground and Background Features in VideosPreprint

Lin, X., Campos, V., Giró-i-Nieto, X., Torres, J., & Ferrer, C. C. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.04092 · Cited by 4

Class Subset Selection for Transfer Learning using SubmodularityPreprint

Manjunatha, V., Ramalingam, S., Marks, T. K., & Davis, L. S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.00060 · Cited by 4

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

NNCubes: Learned Structures for Visual Data Exploration.Preprint

Wang, Z., Cashman, D., Li, M., Li, J., Berger, M., Levine, J. A., Chang, R., & Scheidegger, C. (2018) · arXiv (Cornell University) · Cited by 3

Spatiotemporal Feature Learning for Event-Based VisionPreprint

Ghosh, R., Gupta, A. K., Tang, S., Soares, A. B., & Thakor, N. V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.06923 · Cited by 3

A Deep Learning Framework for Lifelong Machine LearningPreprint

Ling, C. X., & Bohn, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.00157 · Cited by 5

Learning Cross-modal Contrastive Features for Video Domain AdaptationPreprint

Kim, D., Tsai, Y., Zhuang, B., Yu, X., Sclaroff, S., Saenko, K., & Chandraker, M. K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.11974 · Cited by 3

Segment Anything Meets Universal Adversarial PerturbationPreprint

Han, D., Zheng, S., & Zhang, C. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4786251 · Cited by 3

Understanding Physical Dynamics with Counterfactual World Modeling

Venkatesh, R., Chen, H., Feigelis, K. T., Bear, D. M., Jedoui, K., Kotar, K., Binder, F., Lee, W., Liu, S., Smith, K. A., et al. (2023) · European Conference on Computer Vision · doi:10.1007/978-3-031-72691-0_21 · Cited by 18

LoG-staging: a rectal cancer staging method with LoG operator based on maximization of mutual information

ZHANG, G., Dang, H., Zuo, Q., & Tian, Z. (2025) · BMC Medical Imaging · doi:10.1186/s12880-025-01610-7 · Cited by 2

Visual diagnostics for female genital schistosomiasis and the opportunity for improvement using computer vision

Lemin, M. E., Bustinduy, A. L., & Roberts, C. H. (2025) · Parasitology · doi:10.1017/s0031182025100826 · Cited by 2

Cardiac Dynamic Characteristics Classification on Cine MRI Using Semi-supervised Imaging Approach

Ahmad, F., Xiong, J., Wu, J., Xia, J., & Xia, Z. (2025) · Machine Intelligence Research · doi:10.1007/s11633-024-1534-0 · Cited by 2

J-Mac: Jacobian Matrix Meets Masked Contrastive Learning for Generalization in Reinforcement Learning

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