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 701–777 of 777

Reinforcement learning for combinatorial optimization : leveraging uncertainty, structure and priors
Grinsztajn, N. (2023) · theses.fr (ABES) · doi:10.70675/596ed207zce1az43dez9b5fzb69d2c21eb2c · Cited by 1
Implicit Label Augmentation on Partially Annotated Clips via Temporally-Adaptive Features LearningPreprint
Lu, Y., Tang, Z., & Javidi, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.10000
Supervise Thyself: Examining Self-Supervised Representations in Interactive EnvironmentsPreprint
Racah, E., & Pal, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.11951 · Cited by 3
Contribution of artificial metaplasticity to pattern recognition
Fombellida, J. (2018) · doi:10.20868/upm.thesis.51494
Sequencing the musical sections with deep learning
Cui, X., Liao, M., Lv, P., & Zheng, C. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8851935
A Multi-view Perspective of Self-supervised LearningPreprint
Geng, C., Tan, Z., & Chen, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.00877 · Cited by 1
A Collective Learning Framework to Boost GNN ExpressivenessPreprint
Hang, M., Neville, J., & Ribeiro, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.12169
Self-Supervised Video Representation Learning by Recurrent Networks and Frame Order Prediction
Nagabandi, S. S. (2020) · RIT Scholar Works (Rochester Institute of Technology)
Self-supervised learning using consistency regularization of spatio-temporal data augmentation for action recognitionPreprint
Wang, J., Lin, Y., & Ma, A. J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.02086
Learning to Sort Image Sequences via Accumulated Temporal DifferencesPreprint
Kanojia, G., & Raman, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.11649
HILONet: Hierarchical Imitation Learning from Non-Aligned ObservationsPreprint
Liu, S., Cao, J., Chen, W., Wen, L., & Liu, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.02671 · Cited by 1
Latent Space Conditioning on Generative Adversarial Networks
Durall, R., Ho, K., Pfreundt, F., & Keuper, J. (2021) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0010178800240034
A unified view for unsupervised representation learning with density ratio estimation: Maximization of mutual information, nonlinear ICA and nonlinear subspace estimationPreprint
Sasaki, H., & Takenouchi, T. (2021) · arXiv (Cornell University)
Contrastive Learning of Image Representations with Cross-Video Cycle-ConsistencyPreprint
Wu, H., & Wang, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.06463
Human pose estimation and action recognition based on monocular video inputs
Leong, M. C. (2019) · doi:10.32657/10356/136596
From Single to Multiple: Leveraging Multi-level Prediction Spaces for Video ForecastingPreprint
Lan, M., Ning, S., Li, Y., Chen, Q., Chen, X., Han, X., & Cui, S. R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.10068
Self-Supervised Learning Framework for Remote Heart Rate Estimation Using Spatiotemporal Augmentation.Preprint
Wang, H., Ahn, E., & Kim, J. (2021) · arXiv (Cornell University)
Improving self-supervision in video representation learning
Liu, H. (2021) · doi:10.32657/10356/152209
OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised LearningPreprint
Gidaris, S., Bursuc, A., Puy, G., Nikos Komodakis, A., Cord, M., & Pérez, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.11552
Temporally-Embedded Self-Supervised Video Representation Learning
Wang, F., & Liu, H. (2021) · IEEE International Conference on Unmanned Systems (ICUS) · doi:10.1109/icus52573.2021.9641272
Sorting of Scrambled Video Frames Using Temporal Order Verification
Khodier, M., Abdelaziz, A., Gadelkarim, M., Abdelkhalek, A., & Gomaa, W. E. (2022) · International Conference on Ubiquitous Information Management and Communication (IMCOM) · doi:10.1109/imcom53663.2022.9721759
Time-based self-supervised learning for Wireless Capsule Endoscopy
Pascual, G., Laiz, P., García, A., Wenzek, H. R., Vitria, J. M., & Seguí, S. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105631
Meta-Contrast Self-Supervised Video Representation Learning Based on Multi-Task Spatio-Temporal Representation Fusion and Scale DecouplingPreprint
Zhang, Y., HE, G., & FAN, S. X. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4162678
Online Deep Clustering with Video Track Consistency
Alfani, A., Becattini, F., Seidenari, L., & Bimbo, A. D. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956284
Which One is Better? Self-supervised Temporal Coherence Learning for Skeleton Based Action Recognition
Wu, B., Wu, M., Ji, H., & Shen, L. (2022) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb54206.2022.10007979
Semantic Fast-Forwarding for Video Training Set Construction
Feng, Z., & Satyanarayanan, M. (2023) · IEEE Workshop on Mobile Computing Systems and Applications (WMCSA) · doi:10.1145/3572864.3580331
Two-Stream Auto-Encoder Network for Unsupervised Skeleton-Based Action Recognition
Wang, G., Guan, Y., & Li, D. (2023) · Journal of Shanghai Jiaotong University (Science) · doi:10.1007/s12204-023-2619-6
Self-Supervised Temporal Consistency applied to Domain Adaptation in Semantic Segmentation of Urban Scenes
Barbosa, F. M., & Osório, F. S. (2023) · Latin American Robotics Symposium, Brazilian Symposium on Robotics and Workshop on Robotics in Education (LARS/SBR/WRE) · doi:10.1109/lars/sbr/wre59448.2023.10333056
[Paper] PSp-Transformer: A Transformer with Data-level Probabilistic Sparsity for Action Representation Learning
Zhou, J., & Komuro, T. (2023) · ITE Transactions on Media Technology and Applications · doi:10.3169/mta.12.123
Automatic visual inspection of mechanical assemblies via 3Dpoint cloud classification with deep neural networksPreprint
Boughrara, A., Jovančević, I., Došljak, V., Orteu, J., & Belloc, M. (2024) · Research Square · doi:10.21203/rs.3.rs-3897234/v1
Contextual attention U-net: a deep learning-based model for decoding brain characteristics of smokers
Deng, H., Kuang, X., & Lin, F. (2024) · MIPPR: Parallel Processing of Images and Optimization Techniques; and Medical Imaging · doi:10.1117/12.2691200
United We Stand, Divided We Fall: UnityGraph for Unsupervised Procedure Learning from Videos
Bansal, S., Arora, C., & Jawahar, C. V. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00638
ASGSA: global semantic-aware network for action segmentation
Bian, Q., Zhang, C., Ren, K., Yue, T., & Zhang, Y. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09776-x
Active Uncertainty Representation Learning: Toward More Label Efficiency in Deep Learning
Mohamadi, S. (2024) · doi:10.33915/etd.12295
Feature representation learning for human activity recognition
Khaertdinov, B. (2024) · Research Publications (Maastricht University) · doi:10.26481/dis.20240618bk
Unsupervised Feature Learning for Video Understanding
Wu, Z., & Jiang, Y. (2024) · Wireless networks · doi:10.1007/978-3-031-57679-9_6
SPViM: Sparse Pyramid Video Representation Learning Framework for Fine-Grained Action Retrieval
Wang, L., Yang, C., Luan, H., Gai, W., Geng, W., & Zheng, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5594-3_27
Self-Supervised Representation Learning for Sleep Stage Classification with Feature Space Augmentation and Temporal Prediction
Jiang, Q., Chen, L., Gao, H. Z., Shen, F., & Guo, H. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651113
Audio-Visual Self-Supervision for Frame-Level Player-wise Offensive Shot Detection in Table Tennis Matches
Tsuji, S., Hashimoto, A., Yang, M., Ma, J., Honda, H., & Tanaka, K. (2024) · doi:10.1145/3689061.3689064
Learning Features by Minimizing the Interframe Differences
Zhao, D., & Zhang, D. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-9919-0_16
Automatic Evolutionary Clustering for Human Activity Discovery
Lai, D. T. C., & Hadikhani, P. (2024) · Springer eBooks · doi:10.1007/978-981-97-7679-5_4
Multi-Head AdaIN Audio-to-Video Motion Style Transfer
Aldausari, N., Sowmya, A., & Mohammadi, G. (2024) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio64047.2024.10907605 · Cited by 1
Progressive self-supervised spatio-temporal feature learning based on video sequence saliency
kang, J., Xu, T., qu, B., Wang, X., Lian, X., Guo, J., & Gao, Y. (2025) · International Conference on Video and Image Processing (ICVIP) · doi:10.1117/12.3059113
Enhancing Biomedical Image Labelling with Self-supervised Learning
Tan, N. D., Tran, H. P., Tran, T. T.-T., Ly, L. T., Hoang, T. T.-D., Bao, P. T., & Sang, V. N. T. (2025) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-031-90194-2_70
FuzzyPR: Efficient Person Retrieval Using Fuzzy Semantic Descriptions Under Surveillance Scenario
Sun, M., Luo, C., Ren, J., & Zhang, Y. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3579398
Attention in Machine Learning
Gille, C. (2025) · Springer eBooks · doi:10.1007/978-3-031-84300-6_12
SAGCN: Self-Attention Graph Convolutional Network for Human Pose Embedding
Xu, Z., Hong, J., Yu, Y., Lin, C., Yu, L., & Xu, M. (2025) · IEEE Access · doi:10.1109/access.2025.3596651
Multi-Entity Video Transformers for Fine-Grained Video Representation Learning
Walmer, M., Kanjirathinkal, R., Tai, K. S., Muzumdar, K., Tian, T.-P., & Shrivastava, A. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00198
ViDROP: Video Dense Representation Through Spatio-Temporal Sparsity
Sameni, S., Jenni, S., & Favaro, P. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00300
Advancing human pose estimation through interdisciplinary physics-inspired deep learning models
Shao, H., Zhang, X., & Li, L. (2025) · Frontiers in Physics · doi:10.3389/fphy.2025.1558325 · Cited by 1
EAST: Edge-Aware Self-Supervised Transformer for Privacy-Compliant Video Analysis
Zhicai, L., Abdullah, S. N. H. S., Jiale, Q., & Ghani, A. T. A. (2025) · International Conference on Cyber Resilience (ICCR) · doi:10.1109/iccr67387.2025.11291780
Reinforcement Learning Meets Masked Video Modeling : Trajectory-Guided Adaptive Token Selection
Rai, A. K., Min, K., Krishna, T., Hu, F., Smeaton, A. F., & O'Connor, N. E. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00730
Piecing It Together: A Unified Diffusion Framework for Jigsaw Puzzle Reconstruction
Fotis, V., Romanelis, I., & Μουστάκας, Κ. (2025) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip67698.2025.11396878
SSL-AD: Spatiotemporal Self-supervised Learning for Generalizability and Adaptability Across Alzheimer’s Prediction Tasks and Datasets
Kaczmarek, E., Szeto, J., Nichyporuk, B., & Arbel, T. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-16128-4_1
Human Action Recognition in Videos with Shallow and Deep Learning
Phan, T. H. H. (2019) · theses.fr (ABES) · doi:10.70675/fa813329z9321z4fc2za918zd4282f6ec3ac
Structured deep learning for video analysis
Baradel, F. (2020) · theses.fr (ABES) · doi:10.70675/2a8e4991z61e3z4030z8b4cz030a297ed81c
Large-scale learning from video and natural language
Miech, A. (2020) · theses.fr (ABES) · doi:10.70675/e1c0d395ze48fz4d9bz821ezf11900bb536d
Contributions to representation learning of multivariate time series and graphs
Pineau, E. (2020) · theses.fr (ABES) · doi:10.70675/f0b58758z7fe5z4704zac03za7954d6312b3
Enabling real-world EEG applications with deep learning
Banville, H. J. (2022) · theses.fr (ABES) · doi:10.70675/12e08edaz0021z43fbzb6adz07d315fc6ee2
Learning to localize goal-oriented actions with weak supervision
Zhukov, D. (2021) · doi:10.70675/46949df1z20a0z45b9zaa69ze59147934f9d
Méthodes d'apprentissage profond pour l’estimation de paramètres météorologiques à partir d'images webcam. Applications au suivi des épisodes de neige en plaine
Lepetit, P. (2021) · theses.fr (ABES) · doi:10.70675/9c876d87z919cz4784z894bz0deee726d39d
Self-supervised learning of deep visual representations
Caron, M. (2021) · theses.fr (ABES) · doi:10.70675/40d50cc9zaa9bz46aez992bz262e84a1f57d
Video question answering with limited supervision
Engin, D. (2024) · theses.fr (ABES) · doi:10.70675/c9c7dcf2z4ca3z4e91z9d27zdacd00f76581
Learning effective video representations for action recognition
Yang, D. (2024) · theses.fr (ABES) · doi:10.70675/4046013fz7818z4acdza84bze9be293f2334
Self-supervised representation learning and applications to image and video analysis
Denize, J. (2023) · theses.fr (ABES) · doi:10.70675/b90e077ezf81ez4870z9d3fz2e783ba7438e
Seeing Fast and Slow: Learning the Flow of Time in Videos
Wu, Y., Luo, R., Zhu, J., Tu, T., Farhadi, A., Wallingford, M., Wang, Y.-C. F., Marschner, S., & Ma, W.-C. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37335-9_29 · Cited by 1
Self-supervised Learning for Audio Signal
Sun, Y., Xu, Q., Zhu, B., Chen, W., Sun, Y., & Xu, K. (2026) · Springer eBooks · doi:10.1007/978-981-95-0836-5_4
VideoAlign: A Toolkit to Make Video Analysis Accessible EICS013
Belo, J. M. E., Oei, K., & Feit, A. M. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3816765
Beyond landmarks: modeling backchannel agreement via reconstruction-based holistic facial motion representations
Huang, Y., Fu, E. Y., & Ng, P. H. F. (2026) · Journal on Image and Video Processing · doi:10.1186/s13640-026-00700-y
LP 2 DH: A Locality-Preserving Pixel-Difference Hashing Framework for Dynamic Texture Recognition
Ding, R., Ren, J., Yu, H., Li, J., & Jiang, X. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3718418
A CPU-Efficient Compression-Robust Deepfake Detection Framework via Temporal Inconsistency Reformulation
Umamageswaran, J., & Sarvesh, G. (2026) · International Conference on Data Science and Business Systems (ICDSBS) · doi:10.1109/icdsbs69077.2026.11634849
TRACE: Intervention-aware temporal representation learning for video understanding
Chaudhry, H. N., Kulsoom, F., Mohsin, S. M., Aslam, S., & Mohsin, S. M. (2026) · Machine Learning with Applications · doi:10.1016/j.mlwa.2026.100976
Self-Supervised Vision Transformers for Real-Time Object Tracking in Autonomous Vehicles
Aggarwal, N., & Malhotra, V. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-27845-6_9
Self-Supervised and Multimodal Vision Foundation Models
Zraqou, J. S., & Alkhateeb, J. H. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0136-3.ch011
Ranking-Supervised Adaptation of Vision-Language Models for Temporal Progress Ordering Toward Automated Construction Monitoring
Jeong, G., Kang, K.-I., & Kim, H. (2026) · Buildings · doi:10.3390/buildings16214281
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., & Joulin, A. (2020) · arXiv · doi:10.48550/arxiv.2006.09882 · Cited by 2,499
Self-citation
Contrastive Multiview Coding
Tian, Y., Krishnan, D., & Isola, P. (2019) · arXiv · doi:10.48550/arxiv.1906.05849 · Cited by 465

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