Cited by 779Human Pose and Action RecognitionDomain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

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 access

Misra, Ishan, Zitnick, C. Lawrence, Hebert, Martial·2016·Lecture Notes in Computer Science

DOI 10.1007/978-3-319-46448-0_32

Open this paper in CoChat — free account Open PDF

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

Crossref · 10.1007/978-3-319-46448-0_32

Cites

72 of 72 references verified0 citation errors · 0 not checkable

Statistics

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.

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
Long Short-Term Memory
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
Fast R-CNN
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

Show all 76 references

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

Sorted by citations

Items 101–200 of 777

Video Representation Learning by Recognizing Temporal Transformations
Jenni, S., Meishvili, G., & Favaro, P. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58604-1_26 · Cited by 126
Efficient Visual Pretraining with Contrastive Detection
Hénaff, O. J., Koppula, S., Alayrac, J.-B., van den Oord, A., Vinyals, O., & Carreira, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00993 · Cited by 128
Improvements to Context Based Self-Supervised Learning
Mundhenk, T. N., Ho, D., & Chen, B. Y. (2018) · 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.2018.00973 · Cited by 123
Self-Supervised Spatiotemporal Feature Learning via Video Rotation PredictionPreprint
Jing, L., Yang, X., Liu, J., & Tian, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.11387 · Cited by 179
Towards Long-Form Video Understanding
Wu, C.-Y., & Krähenbühl, P. (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/cvpr46437.2021.00192 · Cited by 131
Geometry Guided Convolutional Neural Networks for Self-Supervised Video Representation Learning
Gan, C., Gong, B., Liu, K., Su, H., & Guibas, L. J. (2018) · 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.2018.00586 · Cited by 124
A Comprehensive Study of Deep Video Action RecognitionPreprint
Zhu, Y., Li, X., Liu, C., Zolfaghari, M., Xiong, Y., Wu, C., Zhang, Z., Tighe, J., Manmatha, R., & Li, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.06567 · Cited by 212
Self-supervised Video Transformer
Ranasinghe, K., Naseer, M., Khan, S. H., Khan, F. S., & Ryoo, M. S. (2022) · 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/cvpr52688.2022.00289 · Cited by 128
Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzles
Wang, G., Wang, Y., Qin, J., Zhang, D., Bao, X., & Huang, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20080-9_29 · Cited by 118
Self-Supervised Representation Learning From Videos for Facial Action Unit Detection
Li, Y., Zeng, J., Shan, S., & Chen, X. (2019) · 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.2019.01118 · Cited by 116
What to Hide from Your Students: Attention-Guided Masked Image Modeling
Kakogeorgiou, I., Gidaris, S., Psomas, B., Avrithis, Y., Bursuc, A., Karantzalos, K. G., & Komodakis, N. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20056-4_18 · Cited by 132
Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data
Sautier, C., Puy, G., Gidaris, S., Boulch, A., Bursuc, A., & Marlet, R. (2022) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52688.2022.00966 · Cited by 188
Instance Localization for Self-supervised Detection Pretraining
Yang, C., Wu, Z., Zhou, B., & Lin, S. C.-F. (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/cvpr46437.2021.00398 · Cited by 126
Self-Supervised Feature Learning by Learning to Spot Artifacts
Jenni, S., & Favaro, P. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00289 · Cited by 115
Video Jigsaw: Unsupervised Learning of Spatiotemporal Context for Video Action Recognition
Ahsan, U., Madhok, R., & Essa, I. A. (2019) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2019.00025 · Cited by 113
DynamoNet: Dynamic Action and Motion Network
Diba, A., Sharma, V., Gool, L. V., & Stiefelhagen, R. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00629 · Cited by 111
Self-Supervised Learning of Point Clouds via Orientation Estimation
Poursaeed, O., Jiang, T., Qiao, H., Xu, N., & Kim, V. G. (2020) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv50981.2020.00112 · Cited by 113
Audio self-supervised learning: A survey
Liu, S., Mallol-Ragolta, A., Parada‐Cabaleiro, E., Qian, K., Jing, X., Kathan, A., Hu, B., & Schuller, B. W. (2022) · Patterns · doi:10.1016/j.patter.2022.100616 · Cited by 134
ActionFlowNet: Learning Motion Representation for Action Recognition
Ng, J. Y.-H., Choi, J., Neumann, J., & Davis, L. S. (2018) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2018.00179 · Cited by 108
Almost Unsupervised Learning for Dense Crowd Counting
Sam, D. B., Sajjan, N. N., Maurya, H., & Babu, R. V. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33018868 · Cited by 102
Robust Audio-Visual Instance Discrimination
Morgado, P. M., Misra, I., & Vasconcelos, N. M. (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/cvpr46437.2021.01274 · Cited by 108
Self-citation
iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis
Liu, X., Shi, H., Chen, H., Yu, Z., Li, X., & Zhao, G. (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/cvpr46437.2021.01049 · Cited by 127
Neural Graph Matching Networks for Fewshot 3D Action Recognition
Guo, M., Chou, E., Huang, D.-A., Song, S., Yeung, S. Y., & Fei-Fei, L. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01246-5_40 · Cited by 110
Form2Fit: Learning Shape Priors for Generalizable Assembly from Disassembly
Zakka, K., Zeng, A., Lee, J., & Song, S. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196733 · Cited by 109
A critical analysis of self-supervision, or what we can learn from a single image
Asano, Y. M., Rupprecht, C., & Vedaldi, A. (2019) · Oxford University Research Archive (ORA) (University of Oxford) · doi:10.48550/arxiv.1904.13132 · Cited by 129
Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework
Li, T., Wang, X., & Yamasaki, T. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413694 · Cited by 108
RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning
Chen, P., Huang, D. Y., He, D., Long, X., Zeng, R., Wen, S., Tan, M., & Gan, C. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i2.16189 · Cited by 108
Recent Advances in Imitation Learning from Observation
Torabi, F., Warnell, G., & Stone, P. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/882 · Cited by 170
SeCo: Exploring Sequence Supervision for Unsupervised Representation Learning
Yao, T., Zhang, Y., Qiu, Z., Pan, Y., & Mei, T. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i12.17274 · Cited by 106
ClusterFit: Improving Generalization of Visual Representations
Yan, X., Misra, I., Gupta, A. K., Ghadiyaram, D., & Mahajan, D. K. (2020) · 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/cvpr42600.2020.00654 · Cited by 109
Self-citation
Self-supervised Learning for Video Correspondence FlowPreprint
Lai, Z., & Xie, W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.00875 · Cited by 104
Large Scale Holistic Video Understanding
Diba, A., Fayyaz, M., Sharma, V., Paluri, M., Gall, J., Stiefelhagen, R., & Gool, L. V. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58558-7_35 · Cited by 106
Self-supervised anomaly detection in computer vision and beyond: A survey and outlook
Hojjati, H., Ho, T. K. K., & Armanfard, N. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106106 · Cited by 149
Learning Representations by Predicting Bags of Visual Words
Gidaris, S., Bursuc, A., Komodakis, N., Pérez, P., & Cord, M. (2020) · 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/cvpr42600.2020.00696 · Cited by 101
Rotation Awareness Based Self-Supervised Learning for SAR Target Recognition With Limited Training Samples
Wen, Z., Liu, Z., Zhang, S., & Pan, Q. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3104179 · Cited by 102
Meta-Learning Update Rules for Unsupervised Representation LearningPreprint
Metz, L., Maheswaranathan, N., Cheung, B., & Sohl‐Dickstein, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.00222 · Cited by 129
Shuffle and Attend: Video Domain Adaptation
Choi, J., Sharma, G., Schulter, S., & Huang, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58610-2_40 · Cited by 93
Labelling unlabelled videos from scratch with multi-modal self-supervision
Asano, Y. M., Patrick, M., Rupprecht, C., & Vedaldi, A. (2020) · Oxford University Research Archive (ORA) (University of Oxford) · doi:10.48550/arxiv.2006.13662 · Cited by 94
2-D Skeleton-Based Action Recognition via Two-Branch Stacked LSTM-RNNs
Avola, D., Cascio, M., Cinque, L., Foresti, G. L., Massaroni, C., & Rodolà, E. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2960588 · Cited by 100
Self-Supervised Vessel Segmentation via Adversarial Learning
Ma, Y., Yang, H., Deng, H., Song, T., Wang, H., Xue, Z., Cao, H., Ma, R., & Guan, H. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00744 · Cited by 93
Removing the Background by Adding the Background: Towards Background Robust Self-supervised Video Representation Learning
Wang, J., Gao, Y., Li, K., Lin, Y., Ma, A. J., Cheng, H., Peng, P., Huang, F., Ji, R., & Sun, X. (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/cvpr46437.2021.01163 · Cited by 99
Align2Ground: Weakly Supervised Phrase Grounding Guided by Image-Caption Alignment
Datta, S., Sikka, K., Roy, A., Ahuja, K., Parikh, D., & Divakaran, A. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00269 · Cited by 90
Broaden Your Views for Self-Supervised Video Learning
Recasens, A., Luc, P., Alayrac, J.-B., Wang, L., Strub, F., Tallec, C., Malinowski, M., Pătrăucean, V., Altché, F., Valko, M., et al. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00129 · Cited by 98
Self-supervised Contrastive Learning for EEG-based Sleep Staging
Jiang, X., Zhao, J., Du, B., & Yuan, Z. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533305 · Cited by 95
Self-supervised Learning for Semi-supervised Time Series Classification
Jawed, S., Grabocka, J., & Schmidt-Thieme, L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-47426-3_39 · Cited by 89
The Surprising Effectiveness of Representation Learning for Visual Imitation
Pari, J., Shafiullah, N. M. (., Arunachalam, S. P., & Pinto, L. (2022) · Robotics: Science and Systems XVIII · doi:10.15607/rss.2022.xviii.010 · Cited by 95
Exploiting deep residual networks for human action recognition from skeletal data
Pham, H., Khoudour, L., Crouzil, A., Zegers, P., & Velastín, S. A. (2018) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2018.03.003 · Cited by 85
Masked Video Distillation: Rethinking Masked Feature Modeling for Self-supervised Video Representation Learning
Wang, R., Chen, D., Wu, Z., Chen, Y., Dai, X., Liu, M., Yuan, L., & Jiang, Y. (2023) · 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/cvpr52729.2023.00611 · Cited by 91
Self-Supervised Pretraining for RGB-D Salient Object Detection
Zhao, X., Pang, Y., Zhang, L., Lu, H., & Ruan, X. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20257 · Cited by 91
Oops! Predicting Unintentional Action in Video
Epstein, D., Chen, B., & Vondrick, C. (2020) · 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/cvpr42600.2020.00100 · Cited by 82
S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation
Zhu, Y., Min, M. R., Kadav, A., & Graf, H. P. (2020) · 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/cvpr42600.2020.00657 · Cited by 87
In the Eye of the Beholder: Gaze and Actions in First Person Video
Li, Y., Liu, M., & Rehg, J. M. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3051319 · Cited by 91
DirecFormer: A Directed Attention in Transformer Approach to Robust Action Recognition
Truong, T.-D., Bui, Q.-H., Duong, C. N., Seo, H., Phung, S. L., Li, X., & Luu, K. (2022) · 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/cvpr52688.2022.01940 · Cited by 83
VisualEchoes: Spatial Image Representation Learning Through Echolocation
Gao, R., Chen, C., Al-Halah, Z., Schissler, C., & Grauman, K. L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58545-7_38 · Cited by 83
DeepPermNet: Visual Permutation Learning
Cruz, R. S., Fernando, B., Cherian, A., & Gould, S. J. (2017) · 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.2017.640 · Cited by 77
View-Invariant Probabilistic Embedding for Human Pose
Sun, J. J., Zhao, J., Chen, L.-C., Schroff, F., Adam, H., & Liu, T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58558-7_4 · Cited by 79
Survey on deep learning methods in human action recognition
Koohzadi, M., & Charkari, N. M. (2017) · IET Computer Vision · doi:10.1049/iet-cvi.2016.0355 · Cited by 75
Adversarial Self-supervised Learning for Semi-supervised 3D Action Recognition
Si, C., Nie, X., Wang, W., Wang, L., Tan, T., & Feng, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58571-6_3 · Cited by 77
Contrastive Multimodal Fusion with TupleInfoNCE
Liu, Y., Fan, Q., Zhang, S., Dong, H., Funkhouser, T. A., & Yi, L. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00079 · Cited by 77
Deep Learning for Automatic Violence Detection: Tests on the AIRTLab Dataset
Sernani, P., Falcionelli, N., Tomassini, S., Contardo, P., & Dragoni, A. F. (2021) · IEEE Access · doi:10.1109/access.2021.3131315 · Cited by 90
OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning
Gidaris, S., Bursuc, A., Puy, G., Nikos Komodakis, A., Cord, M., & Pérez, P. (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/cvpr46437.2021.00676 · Cited by 86
Curriculum self-paced learning for cross-domain object detection
Soviany, P., Ionescu, R. T., Rota, P., & Sebe, N. (2021) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2021.103166 · Cited by 75
Jigsaw Clustering for Unsupervised Visual Representation Learning
Chen, P., Liu, S., & Jia, J. (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/cvpr46437.2021.01136 · Cited by 80
Subject-Aware Contrastive Learning for Biosignals
Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., & Azemi, E. (2020) · arXiv · doi:10.48550/arxiv.2007.04871 · Cited by 74
Neural Transformation Learning for Deep Anomaly Detection Beyond ImagesPreprint
Chen, Q., Pfrommer, T., Kloft, M., Mandt, S., & Rudolph, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.16440 · Cited by 81
Predicting What You Already Know Helps: Provable Self-Supervised LearningPreprint
Lee, J. D., Lei, Q., Saunshi, N., & Zhuo, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.01064 · Cited by 102
Self-EMD: Self-Supervised Object Detection without ImageNetPreprint
Liu, S., Li, Z., & Sun, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.13677 · Cited by 75
Self-supervised representation learning from electroencephalography signals
Banville, H. J., Albuquerque, I., Hyvärinen, A., Moffat, G., Engemann, D. A., & Gramfort, A. (2019) · HAL · Cited by 71
Self-Supervised Learning for Semi-Supervised Temporal Action Proposal
Wang, X., Zhang, S., Qing, Z., Shao, Y., Gao, C., & Sang, N. (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/cvpr46437.2021.00194 · Cited by 72
Prototypical Contrast and Reverse Prediction: Unsupervised Skeleton Based Action Recognition
Xu, S., Rao, H., Hu, X., Cheng, J., & Hu, B. (2020) · IEEE transactions on multimedia · doi:10.1109/tmm.2021.3129616 · Cited by 77
Unsupervised Representation Learning by Predicting Random Distances
Hu, W., Pang, G., Shen, C., & Ma, C. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/408 · Cited by 72
Spatio-temporal Contrastive Domain Adaptation for Action Recognition
Song, X., Zhao, S., Yang, J., Yue, H., Xu, P., Hu, R., & Chai, H. (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/cvpr46437.2021.00966 · Cited by 71
Dissecting self-supervised learning methods for surgical computer vision
Ramesh, S., Srivastav, V., Alapatt, D., Yu, T., Murali, A., Sestini, L., Nwoye, C. I., Hamoud, I., Sharma, S., Fleurentin, A., et al. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102844 · Cited by 81
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive LearningPreprint
Hyvärinen, A., Sasaki, H., & Turner, R. E. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.08651 · Cited by 67
Hierarchical Transformer: Unsupervised Representation Learning for Skeleton-Based Human Action Recognition
Cheng, Y.-B., Chen, X., Chen, J., Wei, P., Zhang, D., & Lin, L. (2021) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme51207.2021.9428459 · Cited by 68
Learning Cross-Modal Contrastive Features for Video Domain Adaptation
Kim, D., Tsai, Y., Zhuang, B., Yu, X., Sclaroff, S., Saenko, K., & Chandraker, M. K. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01336 · Cited by 68
Unsupervised Action Segmentation by Joint Representation Learning and Online Clustering
Kumar, S., Haresh, S., Ahmed, A., Konin, A., Zia, M. Z., & Tran, Q.-H. (2022) · 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/cvpr52688.2022.01954 · Cited by 72
Contrastive self-supervised learning from 100 million medical images with optional supervision
Ghesu, F. C., Georgescu, B., Mansoor, A., Yoo, Y., Neumann, D., Patel, P. K., Vishwanath, R. S., Balter, J. M., Cao, Y., Grbić, S., et al. (2022) · Journal of medical imaging · doi:10.1117/1.jmi.9.6.064503 · Cited by 111
VideoSSL: Semi-Supervised Learning for Video Classification
Jing, L., Parag, T., Wu, Z., Tian, Y., & Wang, H. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00115 · Cited by 66
SkeletonMAE: Graph-based Masked Autoencoder for Skeleton Sequence Pre-training
Yan, H., Liu, Y., Wei, Y., Li, Z., Li, G., & Lin, L. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00516 · Cited by 76
Only Time Can Tell: Discovering Temporal Data for Temporal Modeling
Sevilla-Lara, L., Zha, S., Yan, Z., Goswami, V., Feiszli, M., & Torresani, L. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00058 · Cited by 68
Self-supervised Video Representation Learning by Context and Motion Decoupling
Huang, L., Yu, L., Wang, B., Pan, P., Xu, Y., & Jin, R. (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/cvpr46437.2021.01367 · Cited by 63
Better and Faster: Knowledge Transfer from Multiple Self-supervised Learning Tasks via Graph Distillation for Video Classification
Zhang, C., & Peng, Y. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/158 · Cited by 64
Learning the Predictability of the Future
Surís, D., Liu, R., & Vondrick, C. (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/cvpr46437.2021.01242 · Cited by 62
Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting
Bhunia, A. K., Chowdhury, P. N., Yang, Y., Hospedales, T. M., Xiang, T., & Song, Y.-Z. (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/cvpr46437.2021.00562 · Cited by 65
Sketch3T: Test-Time Training for Zero-Shot SBIR
Sain, A., Bhunia, A. K., Potlapalli, V., Chowdhury, P. N., Xiang, T., & Song, Y.-Z. (2022) · 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/cvpr52688.2022.00731 · Cited by 65
Cross Pixel Optical-Flow Similarity for Self-supervised Learning
Mahendran, A., Thewlis, J., & Vedaldi, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20873-8_7 · Cited by 59
Motion-aware Contrastive Video Representation Learning via Foreground-background Merging
Ding, S., Li, M., Yang, T., Qian, R., Xu, H., Chen, Q., Wang, J., & Xiong, H. (2022) · 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/cvpr52688.2022.00949 · Cited by 63
Self-Supervised Sim-to-Real Adaptation for Visual Robotic Manipulation
Jeong, R., Aytar, Y., Khosid, D., Zhou, Y., Kay, J., Lampe, T., Bousmalis, K., & Nori, F. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197326 · Cited by 60
Hand Pose Guided 3D Pooling for Word-level Sign Language Recognition
Hosain, A. A., Santhalingam, P. S., Pathak, P. H., Rangwala, H., & Košecká, J. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00347 · Cited by 63
Learning by Aligning Videos in Time
Haresh, S., Kumar, S., Coskun, H., Syed, S. N., Konin, A., Zia, M. Z., & Tran, Q.-H. (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/cvpr46437.2021.00550 · Cited by 61
CMD: Self-supervised 3D Action Representation Learning with Cross-Modal Mutual Distillation
Mao, Y., Zhou, W., Lu, Z., Deng, J., & Li, H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20062-5_42 · Cited by 70
Multi-view Action Recognition Using Cross-View Video Prediction
Vyas, S., Rawat, Y. S., & Shah, M. A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58583-9_26 · Cited by 62
Self-Supervised Learning of Video-Induced Visual Invariances
Tschannen, M., Djolonga, J., Ritter, M., Mahendran, A., Houlsby, N., Gelly, S., & Lučić, M. (2020) · 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/cvpr42600.2020.01382 · Cited by 63
Efficient Spatio-Temporal Contrastive Learning for Skeleton-Based 3-D Action Recognition
Gao, X., Yang, Y., Zhang, Y., Li, M., Yu, J.-G., & Du, S. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3127040 · Cited by 59
Hierarchically Self-supervised Transformer for Human Skeleton Representation Learning
Chen, Y., Zhao, L., Yuan, J., Tian, Y., Xia, Z., Geng, S., Han, L., & Metaxas, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19809-0_11 · Cited by 58
Probabilistic Representations for Video Contrastive Learning
Park, J., Lee, J., Kim, I.-J., & Sohn, K. (2022) · 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/cvpr52688.2022.01430 · Cited by 59
Joint Visual-Temporal Embedding for Unsupervised Learning of Actions in Untrimmed Sequences
VidalMata, R. G., Scheirer, W. J., Kukleva, A., Cox, D., & Kuehne, H. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00128 · Cited by 60
Learning Actionable Representations from Visual Observations
Dwibedi, D., Tompson, J., Lynch, C., & Sermanet, P. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593951 · Cited by 61
DistInit: Learning Video Representations Without a Single Labeled Video
Girdhar, R., Tran, D., Torresani, L., & Ramanan, D. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00094 · Cited by 57

Authors: Misra, I., Zitnick, C. L., & Hebert, M.
Affiliations: Carnegie Mellon University (US)
Meta (United States) (US)
Corresponding author: Misra, Ishan
ORCID: Misra, Ishan
Zitnick, C. Lawrence
Hebert, Martial
Venue: Lecture Notes in Computer Science
Publisher: Springer International Publishing
Published: 2016
Pages: 527-544
ISSN: 0302-9743 (ISSN-L)
1611-3349
DOI: 10.1007/978-3-319-46448-0_32
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
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
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

Shuffle and Learn: Unsupervised Learning Using Temporal Order Verification

Overview

Integrity signals

Verification

Claims the paper makes

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks