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
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891
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
(2010) · Cited by 8,261
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
Two-Stream Convolutional Networks for Action Recognition in Videos
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1406.2199 · Cited by 6,756
Communication in the presence of noise
Shannon, C. E. (1949) · Proceedings of the IRE · doi:10.1109/jrproc.1949.232969 · Cited by 6,539
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694
Dimensionality Reduction by Learning an Invariant Mapping
Hadsell, R., Chopra, S., & LeCun, Y. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.100 · Cited by 5,336
Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841
Communication In The Presence Of Noise
Shannon, C. E. (1998) · Proceedings of the IEEE · doi:10.1109/jproc.1998.659497 · Cited by 4,815
Ucf101: A dataset of 101 human actions classes from videos in the wild
Soomro, K., Zamir, A. R., & Shah, M. (2012) · arXiv · doi:10.48550/arxiv.1212.0402 · Cited by 4,575
Hmdb: a large video database for human motion recognition
Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., & Serre, T. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126543 · Cited by 3,965
DeepPose: Human Pose Estimation via Deep Neural Networks
Toshev, A., & Szegedy, C. (2014) · 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.2014.214 · Cited by 3,395
Return of the Devil in the Details: Delving Deep into Convolutional Nets
Chatfield, K., Simonyan, K., Vedaldi, A., & Zisserman, A. (2014) · Proceedings of the British Machine Vision Conference 2014 · doi:10.5244/c.28.6 · Cited by 3,335
Two-Frame Motion Estimation Based on Polynomial Expansion
Farnebäck, G. (2003) · Lecture notes in computer science · doi:10.1007/3-540-45103-x_50 · Cited by 3,112
2D Human Pose Estimation: New Benchmark and State of the Art Analysis
Andriluka, M., Pishchulin, L., Gehler, P., & Schiele, B. (2014) · 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.2014.471 · Cited by 2,699
Unsupervised Visual Representation Learning by Context Prediction
Doersch, C., Gupta, A., & Efros, A. A. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.167 · Cited by 2,629
Efficient sparse coding algorithms
Lee, H., Battle, A. J., Raina, R., & Ng, A. Y. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7503.003.0105 · Cited by 2,471
A survey on vision-based human action recognition
Poppe, R. (2010) · Image and Vision Computing · doi:10.1016/j.imavis.2009.11.014 · Cited by 2,242
Implicit Learning and Tacit Knowledge
Reber, A. S. (2003) · The MIT Press eBooks · doi:10.7551/mitpress/2834.003.0047 · Cited by 2,013
Salakhutdinov, R., & Hinton, G. E. (2009) · Cited by 1,890
Building high-level features using large scale unsupervised learning
Le, Q. V. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639343 · Cited by 1,695
A Synopsis of Linguistic Theory, 1930-1955
Firth, J. R. (1957) · Medical Entomology and Zoology · Cited by 1,675
Implicit learning and tacit knowledge
Reber, A. S. (1989) · Journal of Experimental Psychology: General · doi:10.1037/0096-3445.118.3.219 · Cited by 1,581
Slow Feature Analysis: Unsupervised Learning of Invariances
Wiskott, L., & B, T. J. S. A. (2002) · Neural Computation · doi:10.1162/089976602317318938 · Cited by 1,358
Communications in the presence of noise
Shannon, C. E. (1949) · Medical Entomology and Zoology · Cited by 1,180
Unsupervised Learning of Visual Representations Using Videos
Wang, X., & Gupta, A. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.320 · Cited by 1,005
Discovering objects and their location in images
Šivic, J., Russell, B. C., Efros, A. A., Zisserman, A., & Freeman, W. T. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.77 · Cited by 973
Articulated Human Detection with Flexible Mixtures of Parts
Yang, Y., & Ramanan, D. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.261 · Cited by 843
Pedestrian Detection with Unsupervised Multi-stage Feature Learning
Sermanet, P., Kavukcuoglu, K., Chintala, S., & LeCun, Y. (2013) · 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.2013.465 · Cited by 762
Learning invariance from transformation sequences
Földiák, P. (1991) · Neural Computation · doi:10.1162/neco.1991.3.2.194 · Cited by 661
Convolutional Learning of Spatio-temporal Features
Taylor, G. W., Fergus, R., LeCun, Y., & Bregler, C. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15567-3_11 · Cited by 652
Using Multiple Segmentations to Discover Objects and their Extent in Image Collections
Russell, B. C., Freeman, W. T., Efros, A. A., Šivic, J., & Zisserman, A. (2006) · 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.2006.326 · Cited by 618
Generative Image Modeling Using Style and Structure Adversarial Networks
Wang, X., & Gupta, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46493-0_20 · Cited by 615
Learning the structure of event sequences.
Cleeremans, A., & McClelland, J. L. (1991) · Journal of Experimental Psychology General · doi:10.1037//0096-3445.120.3.235 · Cited by 584
Unsupervised Discovery of Mid-Level Discriminative Patches
Singh, S., Gupta, A., & Efros, A. A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33709-3_6 · Cited by 517
Learning the structure of event sequences.
Cleeremans, A., & McClelland, J. L. (1991) · Journal of Experimental Psychology General · doi:10.1037/0096-3445.120.3.235 · Cited by 516
From implicit skills to explicit knowledge: a bottom-up model of skill learning
Sun, R., Merrill, E. C., & PETERSON, T. (2001) · Cognitive Science · doi:10.1016/s0364-0213(01)00035-0 · Cited by 474
Agrawal, P., Carreira, J., & Malik, J. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.13 · Cited by 438
MODEC: Multimodal Decomposable Models for Human Pose Estimation
Sapp, B., & Taskar, B. (2013) · 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.2013.471 · Cited by 433
Owens, A., Isola, P., McDermott, J. H., Torralba, A. B., Adelson, E. H., & Freeman, W. T. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.264 · Cited by 376
Blocks That Shout: Distinctive Parts for Scene Classification
Juneja, M., Vedaldi, A., Jawahar, C. V., & Zisserman, A. (2013) · 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.2013.124 · Cited by 373
Deep learning from temporal coherence in video
Mobahi, H., Collobert, R., & Weston, J. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553469 · Cited by 328
Slow feature analysis for human action recognition
Zhang, Z., & Tao, D. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.157 · Cited by 307
Mechanisms of Implicit Learning: Connectionist Models of Sequence Processing
Axel Cleeremans, B. (1993) · Cited by 277
Mid-level Visual Element Discovery as Discriminative Mode Seeking
Doersch, C., Gupta, A., & Efros, A. A. (2013) · neural information processing systems · Cited by 238
Learning Image Representations Tied to Ego-Motion
Jayaraman, D., & Grauman, K. L. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.166 · Cited by 236
Mechanisms of Implicit Learning
Cleeremans, A. (1993) · The MIT Press eBooks · doi:10.7551/mitpress/4528.001.0001 · Cited by 232
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
Sequence learning: from recognition and prediction to sequential decision making
Sun, R., & Giles, C. L. (2001) · IEEE Intelligent Systems · doi:10.1109/mis.2001.1463065 · Cited by 145
Harvesting Mid-level Visual Concepts from Large-Scale Internet Images
Li, Q., Wu, J., & Tu, Z. (2013) · 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.2013.115 · Cited by 142
Slow and Steady Feature Analysis: Higher Order Temporal Coherence in Video
Jayaraman, D., & Grauman, K. L. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.418 · Cited by 136
Learning Discriminative Part Detectors for Image Classification and Cosegmentation
Sun, J. Z., & Ponce, J. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.422 · Cited by 123
Watch and learn: Semi-supervised learning of object detectors from videos
Misra, I., Shrivastava, A., & Hebert, M. (2015) · 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.2015.7298982 · Cited by 113
Self-citationTowards Computational Baby Learning: A Weakly-Supervised Approach for Object Detection
Liang, X., Liu, S., Wei, Y., Liu, L., Lin, L., & Yan, S. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.120 · Cited by 110
A Survey on Model Based Approaches for 2D and 3D Visual Human Pose Recovery
Perez-Sala, X., Escalera, S., Ángulo, C., & Gonzàlez, J. (2014) · Sensors · doi:10.3390/s140304189 · Cited by 67
“Clustering by Composition” – Unsupervised Discovery of Image Categories
Faktor, A., & Irani, M. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33786-4_35 · Cited by 63
Temporal Perception and Prediction in Ego-Centric Video
Zhou, Y., & Berg, T. L. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.511 · Cited by 56
Learning to predict: Exposure to temporal sequences facilitates prediction of future events
Baker, R., Dexter, M., Hardwicke, T. E., Goldstone, A., & Kourtzi, Z. (2013) · Vision Research · doi:10.1016/j.visres.2013.10.017 · Cited by 43
Learning image representations equivariant to ego-motion.Preprint
Jayaraman, D., & Grauman, K. L. (2015) · arXiv (Cornell University) · Cited by 19
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
Transformers in Vision: A Survey
Khan, S., Naseer, M., Hayat, M., Zamir, S. W., Khan, F. S., & Shah, M. (2022) · ACM Computing Surveys · doi:10.1145/3505244 · Cited by 3,595
Unsupervised Learning of Depth and Ego-Motion from Video
Zhou, T., Brown, M., Snavely, N., & Lowe, D. G. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.700 · Cited by 2,910
Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey
Jing, L., & Tian, Y. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2992393 · Cited by 2,036
Tian, Y., Krishnan, D., & Isola, P. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58621-8_45 · Cited by 1,997
Contrastive Learning for Unpaired Image-to-Image Translation
Park, T., Efros, A. A., Zhang, R., & Zhu, J.-Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58545-7_19 · Cited by 1,663
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