The DOI matches its Crossref record; 72 of the 76 references it cites were found in a registry or an index.
Shuffle and Learn: Unsupervised Learning Using Temporal Order Verification
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
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
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
Research works printed but not linked: 6
- Bengio, Y., Thibodeau-Laufer, E., Alain, G., Yosinski, J.: Deep generative stochastic networks trainable by backprop. arXiv preprint arXiv:1306.1091 (2013) · arXiv
- Rezende, D.J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models. arXiv preprint arXiv:1401.4082 (2014) · arXiv
- Isola, P., Zoran, D., Krishnan, D., Adelson, E.H.: Learning visual groups from co-occurrences in space and time. arXiv preprint arXiv:1511.06811 (2015) · arXiv
- Srivastava, N., Mansimov, E., Salakhutdinov, R.: Unsupervised learning of video representations using lstms. arXiv preprint arXiv:1502.04681 (2015) · arXiv
- Vondrick, C., Pirsiavash, H., Torralba, A.: Anticipating the future by watching unlabeled video. arXiv preprint arXiv:1504.08023 (2015) · arXiv
- Wang, L., Xiong, Y., Wang, Z., Qiao, Y.: Towards good practices for very deep two-stream convnets. arXiv preprint arXiv:1507.02159 (2015) · arXiv
Cited by
Sorted by citations
Items 601–700 of 777
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
Self-supervised Multi-view Clustering for Unsupervised Image Segmentation
Fang, T., Liang, Z., Shao, X., Dong, Z., & Li, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86383-8_9 · Cited by 3
Multi-view representation learning from local consistency and global alignment
Si, L., Qiang, W., Li, J., Xu, F., & Sun, F. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.06.069 · Cited by 3
Self-Supervised Cross-Video Temporal Learning for Unsupervised Video Domain Adaptation
Choi, J., Huang, J., & Sharma, G. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956161 · Cited by 3
SVGraph: Learning Semantic Graphs from Instructional Videos
Schiappa, M. C., & Rawat, Y. S. (2022) · IEEE International Conference on Multimedia Big Data · doi:10.1109/bigmm55396.2022.00014 · Cited by 4
Temporal-masked skeleton-based action recognition with supervised contrastive learning
Zhao, Z., Chen, G., & Lin, Y. (2023) · Signal Image and Video Processing · doi:10.1007/s11760-022-02442-6 · 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
Cali-NCE: Boosting Cross-modal Video Representation Learning with Calibrated Alignment
Zhao, N., Jiao, J., Xie, W., & Lin, D. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00672 · Cited by 3
Weakly supervised fine-grained semantic segmentation via spatial correlation-guided learning
Dong, Z., Fang, T., Li, J., & Shao, X. (2023) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2023.103815 · Cited by 3
Bicsi, L., Alexe, B., Ionescu, R. T., & Leordeanu, M. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00102 · Cited by 3
Repeat and learn: Self-supervised visual representations learning by Repeated Scene Localization
Altabrawee, H., & Noor, M. H. M. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110804 · Cited by 3
Dave, I. R., Rizve, M. N., & Shah, M. A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73242-3_22 · Cited by 4
Temporal Inception Architecture for Action Recognition with Convolutional Neural Networks
Zhang, W., Cen, J., & Zheng, H. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545720 · Cited by 3
Audio-Visual Predictive Coding for Self-Supervised Visual Representation Learning
Tellamekala, M. K., Valstar, M., Pound, M. P., & Giesbrecht, T. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9413295 · Cited by 3
Learning from Web Data: Improving Crowd Counting via Semi-Supervised Learning
Peng, T., Li, R., Li, S., & Zhu, P. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412901 · Cited by 3
Self-Supervised Mutual Learning for Video Representation Learning
Wang, J., Li, Y., Hu, J., Yang, X., & Ding, Y. (2021) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme51207.2021.9428338 · Cited by 3
Dynamic-boosting attention for self-supervised video representation learning
Wang, Z., Hou, C., Yue, G., & Yang, Q. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02440-0 · Cited by 3
A Self-Supervised Learning Framework for Sequential Recommendation
Jia, R., Bai, X., Zhou, X., & Pan, S. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9534405 · Cited by 3
Interpretive self-supervised pre-training
Manna, S., Bhattacharya, S., & Pal, U. (2021) · Indian Conference on Computer Vision, Graphics and Image Processing · doi:10.1145/3490035.3490273 · Cited by 3
ParamCrop: Parametric Cubic Cropping for Video Contrastive Learning
Qing, Z., Huang, Z., Zhang, S., Tang, M., Gao, C., Jin, R., Ang, M. H., & Sang, N. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3244126 · Cited by 3
Goulão, M., & Oliveira, A. L. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230359 · Cited by 5
COMBAHO: A deep learning system for integrating brain injury patients in society
García‐Rodríguez, J., Gomez‐Donoso, F., Oprea, S., García-García, A., Cazorla, M., Orts‐Escolano, S., Bauer, Z., Castro-Vargas, J. A., Escalona, F., Ivorra-Piqueres, D., et al. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.02.013 · Cited by 3
Video-to-Image Casting: A Flatting Method for Video Analysis
Chen, X., Gao, C., Yang, F., Wang, X., Yang, Y., & Han, Y. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475424 · Cited by 3
FastPR: One-stage Semantic Person Retrieval via Self-supervised Learning
Sun, M., Ren, J., Wang, X. Y., Zhu, W., & Zhang, Y. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548274 · Cited by 3
Inter-Intra Cross-Modality Self-Supervised Video Representation Learning by Contrastive Clustering
Wei, J., Luo, G., Li, B., & Hu, W. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956697 · Cited by 3
Qi, M., Remelli, E., Salzmann, M., & Fua, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.11260 · Cited by 4
Action Shuffle Alternating Learning for Unsupervised Action SegmentationPreprint
Li, J., & Todorović, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.02116 · Cited by 3
Jigsaw Clustering for Unsupervised Visual Representation LearningPreprint
Chen, P., Liu, S., & Jia, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.00323 · Cited by 3
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
Video alignment using unsupervised learning of local and global featuresPreprint
Fakhfour, N., Shahverdikondori, M., & Mohammadzade, H. (2023) · Research Square · doi:10.21203/rs.3.rs-3457319/v1 · Cited by 4
Wang, P., Wen, J., Si, C., Qian, Y., & Wang, L. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3207577 · Cited by 70
Self-supervised temporal adaptive learning for weakly-supervised temporal action localization
Sheng, J., Yu, J., Li, Z., Li, A., & Ge, Y. (2025) · Information Sciences · doi:10.1016/j.ins.2025.121986 · Cited by 2
Evolutionary Machine Learning Meets Self-Supervised Learning: A Comprehensive Survey
Vinhas, A., Correia, J., & Machado, P. (2026) · IEEE Access · doi:10.1109/access.2026.3717264 · Cited by 3
End-to-End Action Segmentation Transformer
Wang, T., & Todorović, S. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00313 · Cited by 3
A Context Based Deep Temporal Embedding Network in Action Recognition
Koohzadi, M., & Charkari, N. M. (2020) · Neural Processing Letters · doi:10.1007/s11063-020-10248-1 · Cited by 2
VizObj2Vec: Contextual Representation Learning for Visual Objects in Video-frames
Farhan, A., & Hossain, M. S. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9377859 · Cited by 2
Self-Supervised Feature Enhancement: Applying Internal Pretext Task to Supervised Learning
Xie, T., Yang, Y., Ding, Z., Cheng, X., Wang, X., Gong, H., & Liu, M. (2022) · IEEE Access · doi:10.1109/access.2022.3233104 · Cited by 2
A Masked Self-Supervised Pretraining Method for Face Parsing
Li, Z., Cao, L., Wang, H., & Xu, L. (2022) · Mathematics · doi:10.3390/math10122002 · Cited by 2
FGSS: Federated global self-supervised framework for large-scale unlabeled data
Zhang, C. B. C., Xie, Z., Yu, B., Wen, C., & Xie, Y. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110453 · Cited by 2
Spatio-Temporal Crop Aggregation for Video Representation Learning
Sameni, S., Jenni, S., & Favaro, P. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00521 · Cited by 2
Weakly-Supervised Representation Learning for Video Alignment and Analysis
Bar-Shalom, G., Leifman, G., & Elad, M. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00676 · Cited by 5
Kazatzidis, S., & Mehrkanoon, S. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650151 · Cited by 4
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
LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving
Kong, L., Xu, X., Liu, Y.-C., Cen, J., Chen, R.-N., Zhang, W.-W., Pan, L., Chen, K., & Liu, Z.-W. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3617126 · Cited by 23
Generative adversarial network for video analytics
Sasithradevi, A., Roomi, S. M. M., & Sivaranjani, R. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-823519-5.00008-7 · Cited by 2
Self-Supervised Learning in Multi-Task Graphs through Iterative Consensus Shift
Haller, E., Burceanu, E., & Leordeanu, M. (2021) · British Machine Vision Conference (BMVC) · doi:10.5244/c.35.130 · Cited by 2
Unsupervised Learning in Space and Time over Several Generations of Teacher and Student Networks
Leordeanu, M. (2020) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-030-42128-1_7 · Cited by 2
Ghelmani, A., & Hammad, A. (2022) · EG-ICE International Workshop on Intelligent Computing in Engineering (EG-ICE) · doi:10.7146/aul.455.c225 · Cited by 2
Active Contrastive Set Mining for Robust Audio-Visual Instance Discrimination
Xuan, H., Xu, Y., Chen, S., Wu, Z., Yang, J., Yan, Y., & Alameda-Pineda, X. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/506 · Cited by 2
MoQuad: Motion-focused Quadruple Construction for Video Contrastive Learning
Liu, Y., Chen, J., & Wu, H. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25069-9_2 · Cited by 2
Weakly-Guided Self-Supervised Pretraining for Temporal Activity Detection
Kahatapitiya, K., Ren, Z., Li, H., Wu, Z., Ryoo, M. S., & Hua, G. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i1.25189 · Cited by 6
Learning Spatio-temporal Representation by Channel Aliasing Video Perception
Lin, Y., Wang, J., Zhang, M., & Ma, A. J. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475394 · Cited by 2
Fuhrmann, H., Boyko, A., Abdelpakey, M. H., & Shehata, M. (2021) · IEEE World Forum on Internet of Things (WF-IoT) · doi:10.1109/wf-iot51360.2021.9594927 · Cited by 2
Efficient, end-to-end and self-supervised methods for speech processing and generation
Pascual, S. (2020) · doi:10.5821/dissertation-2117-180792 · Cited by 2
Enabling the Network to Surf the InternetPreprint
Li, Z., Wang, H., Świstek, T., Chen, W., Li, Y., & Wang, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.12205 · Cited by 6
Kalayeh, M. M., Kamath, N., Liu, L., & Chandrashekar, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.08513 · Cited by 2
Self-Supervised Audio-Visual Representation Learning with Relaxed Cross-Modal Temporal SynchronicityPreprint
Sarkar, P., & Etemad, A. (2021) · arXiv (Cornell University) · Cited by 2
Self-supervised Pretraining with Classification Labels for Temporal Activity DetectionPreprint
Kahatapitiya, K., Ren, Z., Li, H., Wu, Z., & Ryoo, M. S. (2021) · arXiv (Cornell University) · Cited by 2
SKID: Self-Supervised Learning for Knee Injury Diagnosis from MRI DataPreprint
Manna, S., Bhattacharya, S., & Pal, U. (2022) · doi:10.36227/techrxiv.21382737.v1 · Cited by 2
Context-Aware Temporal Embedding of Objects in Video DataPreprint
Farhan, A., & Hossain, M. S. (2024) · Research Square · doi:10.21203/rs.3.rs-4271928/v1 · Cited by 2
A Cross-modal image retrieval method based on contrastive learning
Wen, Z. (2023) · Journal of Optics · doi:10.1007/s12596-023-01382-9 · Cited by 1
VATE: A Large Scale Multimodal Spontaneous Dataset for Affective Evaluation
Agnelli, F., Grossi, G., D’Amelio, A., Paoli, M. D., & Lanzarotti, R. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91575-8_13 · Cited by 1
SAND: A New Programming Abstraction for Video-based Deep Learning
Ye, J., Lee, S., Lim, H., Lee, J., Hong, U., Kwon, Y., & Han, D. (2025) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/3731569.3764847 · Cited by 1
Çelik, A. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-04887-x · Cited by 1
Dual-Path Self-Supervised Spatiotemporal Learning for Satellite Video Scene Understanding
Wang, Y., Li, J., Gao, G., & Gu, Y. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3660976 · Cited by 1
BIMM: Brain-Inspired Masked Modeling for Video Representation Learning
Zhang, J., Wan, Z., Nie, S., Li, C., Shan, S., & Chen, X. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3683701 · Cited by 1
Yang, R., Chen, L., Wang, Z., Wei, L., Wu, M., Zhu, D., & Zhang, Y. (2026) · Nano Letters · doi:10.1021/acs.nanolett.6c00655 · Cited by 1
Unsupervised Learning for Industrial Robot Health Monitoring: Trends, Techniques, and Challenges
Elahi, M. U., Khan, R. T. A., Yazdani, M. H., & Kim, H. S. (2026) · Mathematics · doi:10.3390/math14132397 · Cited by 1
Callemein, T., Roussel, T., Diba, A., Feyter, F. D., Boes, W., Eycken, L. V., Gool, L. V., hamme, H. V., Tuytelaars, T., & Goedemé, T. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09616-9 · Cited by 1
Video representation learning by identifying spatio-temporal transformations
Geng, S., Zhao, S., & Hu, L. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02790-9 · Cited by 1
Self-Supervised Learning for Action Recognition by Video Denoising
Phung, T. T. T., Thu, T. H., Nguyen, V. T., & Vu, D. Q. (2021) · RIVF International Conference on Computing and Communication Technologies (RIVF) · doi:10.1109/rivf51545.2021.9642129 · Cited by 1
Self-Supervised Video Representation Learning with Motion-Contrastive Perception
Liu, J., Cheng, Y., Zhang, Y., Zhao, R.-W., & Feng, R. (2022) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme52920.2022.9859802 · Cited by 2
Time-coherent embeddings for Wireless Capsule Endoscopy
Pascual, G., Vitria, J. M., & Seguí, S. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956652 · Cited by 1
Sparse Subspace Learning Based on Learnable Constraints for Image Clustering
Zhao, S. (2023) · IEEE Access · doi:10.1109/access.2023.3298693 · Cited by 1
CT-VOS: Cutout prediction and tagging for self-supervised video object segmentation
Kini, J., Khan, F. S., Khan, S. H., & Shah, M. A. (2023) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2023.103860 · Cited by 2
Deepfake Video Detection Using LSTM and XRESNET
Shrivathsa, V. P. (2023) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2023.57056 · Cited by 1
Masked Autoencoders are Secretly Efficient Learners
Wei, Z., Wei, C., Mei, J., Bai, Y., Wang, Z., Li, X., Zhu, H., Wang, H., Yuille, A., Zhou, Y., et al. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00797 · 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
Retro-Actions: Learning 'Close' by Time-Reversing 'Open' Videos
Price, W., & Damen, D. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00173 · 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
Adversarial Semi-supervised Multi-domain Tracking
Meshgi, K., & Mirzaei, M. S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69532-3_37 · Cited by 1
Self-supervised Visual Feature Learning and Classification Framework: Based on Contrastive Learning
Wang, Z., Shen, Y., Zhang, X., & Lobo, N. D. V. (2020) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv50220.2020.9305340 · Cited by 1
Unveiling unexpected training data in internet video
Dekel, T., & Snavely, N. (2021) · Communications of the ACM · doi:10.1145/3431283 · Cited by 1
Riand, M., Dollé, L., & Callet, P. L. (2021) · International Conference on Robotics, Computer Vision and Intelligent Systems (ROBOVIS) · doi:10.5220/0010689500003061 · Cited by 1
Zhou, W., Hou, Y., Ouyang, K., & Zhou, S. (2021) · IET Computer Vision · doi:10.1049/cvi2.12084 · Cited by 1
PreViTS: Contrastive Pretraining with Video Tracking Supervision
Chen, B., Selvaraju, R. R., Chang, S., Niebles, J. C., & Naik, N. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00161 · Cited by 2
BRL: Learning behavior representations of Reversi players
Pan, C.-F., Min, F., Zhang, H., & Wang, Y. (2022) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa54385.2022.10032377 · Cited by 1
Self-Supervised Representation Learning for Detection of ACL Tear Injury in Knee MRI.Preprint
Manna, S., Bhattacharya, S., & Pal, U. (2020) · arXiv (Cornell University) · Cited by 1
Yu, H., Huang, Y., Pi, L., & Wang, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.05856 · Cited by 1
Unsupervised Learning on Monocular Videos for 3D Human Pose Estimation.Preprint
Honari, S., Constantin, V., Rhodin, H., Salzmann, M., & Fua, P. (2020) · arXiv (Cornell University) · Cited by 1
Self-supervised Learning with Fully Convolutional NetworksPreprint
Yang, Z., Yu, H., He, Y., Mao, Z., & Mian, A. S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.10017 · Cited by 1
Spatio-Temporal Video Representation Learning for AI Based Video Playback Style PredictionPreprint
Parihar, R., Ramola, G., Saha, R., Kini, R. P. M., Rege, A., & Velusamy, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.01015 · Cited by 1
Inter-intra Variant Dual Representations forSelf-supervised Video RecognitionPreprint
Zhang, L., She, Q., Shen, Z., & Wang, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.01194 · Cited by 1
MIO : Mutual Information Optimization using Self-Supervised Binary Contrastive LearningPreprint
Manna, S., Pal, U., & Bhattacharya, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.12664 · Cited by 2
Autotuned Classification Based on Knowledge Transferred from Self-supervised ModelsPreprint
Kishore, J., & Mukherjee, S. (2022) · Research Square · doi:10.21203/rs.3.rs-1600269/v1 · Cited by 1
Building generalizable deep learning solutions for mobile sensing
Xu, H. (2024) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/174809 · Cited by 1
Forest, F. (2021) · theses.fr (ABES) · doi:10.70675/5187c565z8bbaz452bzaa6ez7a367a0b16f0 · Cited by 1
Metadata
- Authors
- Misra, I., Zitnick, C. L., & Hebert, M.
- Affiliations
- Carnegie Mellon University (US)
- Meta (United States) (US)
- Corresponding author
- Misra, Ishan
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 527-544
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2487442924
- Cited by
- 779
- References
- 76
- Citations by year
- 2026: 18 · 2025: 31 · 2024: 55 · 2023: 98 · 2022: 144 · 2021: 167 · 2020: 131 · 2019: 75 · 2018: 33 · 2017: 23 · 2016: 4
- FWCI
- 15.61
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Nvidia
- Multidisciplinary University Research Initiative
- Office of Naval Research (N00014-16-1-2007)
- Army Research Laboratory (W911NF, W911NF-10, W911NF-10-2-0016)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- unsupervised learning, video representation learning, spatiotemporal signals, pre-training, human action recognition, human pose estimation, visual representation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
