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 301–400 of 777

Test of Time: Instilling Video-Language Models with a Sense of Time
Bagad, P., Tapaswi, M., & Snoek, C. G. M. (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/cvpr52729.2023.00247 · Cited by 31
Self-Supervised Representation Learning for Videos by Segmenting via Sampling Rate Order Prediction
Huang, J., Huang, Y., Wang, Q., Yang, W., & Meng, H. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3114209 · Cited by 26
Predicting Embryo Viability Based on Self-Supervised Alignment of Time-Lapse Videos
Kragh, M. F., Rimestad, J., Lassen, J. T., Berntsen, J., & Karstoft, H. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3116986 · Cited by 26
Static and Dynamic Concepts for Self-supervised Video Representation Learning
Qian, R., Ding, S., Liu, X., & Lin, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19809-0_9 · Cited by 26
ViewCLR: Learning Self-supervised Video Representation for Unseen Viewpoints
Das, S., & Ryoo, M. S. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00553 · Cited by 26
Self-Supervised Time Series Representation Learning by Inter-Intra Relational ReasoningPreprint
Fan, H., Zhang, F., & Gao, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.13548 · Cited by 38
Self-supervised 4D Spatio-temporal Feature Learning via Order Prediction of Sequential Point Cloud Clips
Wang, H., Yang, L., Rong, X., Feng, J., & Tian, Y. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00381 · Cited by 28
Learning to Track Instances without Video Annotations
Fu, Y. L., Liu, S., Iqbal, U., Mello, S. D., Shi, H., & Kautz, 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/cvpr46437.2021.00857 · Cited by 28
Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency
Wu, H., & Wang, X. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00999 · Cited by 26
Cross-Architecture Self-supervised Video Representation Learning
Guo, S., Xiong, Z., Zhong, Y., Wang, L., Guo, X., Han, B., & Huang, W. (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/cvpr52688.2022.01867 · Cited by 25
Noise Estimation Using Density Estimation for Self-Supervised Multimodal Learning
Amrani, E., Ben‐Ari, R., Rotman, D., & Bronstein, A. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i8.16822 · Cited by 35
DrasCLR: A self-supervised framework of learning disease-related and anatomy-specific representation for 3D lung CT images
Yu, K., Sun, L., Chen, J., Reynolds, M. J., Chaudhary, T., & Batmanghelich, K. N. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.103062 · Cited by 26
Prospective Role of Foundation Models in Advancing Autonomous Vehicles
Wu, J., Gao, B., Gao, J., Yu, J., Chu, H., Yu, Q., Gong, X., Chang, Y. M., Tseng, H. E., Chen, H., et al. (2024) · Research · doi:10.34133/research.0399 · Cited by 27
Unsupervised Intuitive Physics from Visual Observations
Ehrhardt, S., Monszpart, Á., Mitra, N. J., & Vedaldi, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20893-6_44 · Cited by 25
Large-Scale Object Mining for Object Discovery from Unlabeled Video
Ošep, A., Voigtlaender, P., Luiten, J., Breuers, S., & Leibe, B. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793683 · Cited by 24
Exploring Relations in Untrimmed Videos for Self-Supervised Learning
Luo, D., Zhou, Y., Fang, B., Zhou, Y., Wu, D., & Wang, W. (2022) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3473342 · Cited by 24
Echo-SyncNet: Self-Supervised Cardiac View Synchronization in Echocardiography
Dezaki, F. T., Luong, C. L., Ginsberg, T., Rohling, R. N., Gin, K., Abolmaesumi, P., & Tsang, T. S. M. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3071951 · Cited by 24
TATL: Task agnostic transfer learning for skin attributes detection
Nguyen, M.-D., Nguyen, T., Vu, H., Pham, Q., Nguyen, M.-D., Nguyen, B. T., & Sonntag, D. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102359 · Cited by 24
Enhancing Audio-Visual Association with Self-Supervised Curriculum Learning
Zhang, J., Xu, X., Shen, F., Lu, H., Liu, X., & Shen, H. T. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i4.16447 · Cited by 24
Learning to Align Sequential Actions in the Wild
Liu, W., Tekin, B., Coskun, H., Vineet, V., Fua, P., & Pollefeys, M. (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/cvpr52688.2022.00222 · Cited by 25
TransRank: Self-supervised Video Representation Learning via Ranking-based Transformation Recognition
Duan, H., Zhao, N., Chen, K., & Lin, D.-H. (2022) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52688.2022.00301 · Cited by 28
Self-Supervised Learning for Semi-Supervised Temporal Language Grounding
Luo, F., Chen, S., Chen, J., Wu, Z., & Jiang, Y. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3228167 · Cited by 25
Spatio-temporal fusion and contrastive learning for urban flow prediction
Zhang, X., Gong, Y., Zhang, C., Wu, X., Guo, Y., Lü, W., Zhao, L., & Dong, X. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111104 · Cited by 24
Learning Video Representations from Textual Web SupervisionPreprint
Stroud, J., Lu, Z., Sun, C., Deng, J., Sukthankar, R., Schmid, C., & Ross, D. A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.14937 · Cited by 45
Self-Supervised Video Representation Learning with Constrained Spatiotemporal Jigsaw
Huo, Y., Ding, M., Lu, H., Huang, Z., Tang, M., Lu, Z., & Xiang, T. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/104 · Cited by 23
Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition
Tang, Y.-S., Liu, X., Yu, X.-M., Zhang, D., Lu, J.-W., & Zhou, J. (2022) · ACM Trans. Multim. Comput. Commun. Appl. · doi:10.1145/3472722 · Cited by 26
Unsupervised Visual Representation Learning by Synchronous Momentum Grouping
Pang, B., Zhang, Y., Li, Y., Cai, J., & Lu, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20056-4_16 · Cited by 25
Hierarchical Self-supervised Representation Learning for Movie Understanding
Xiao, F., Kundu, K., Tighe, J., & Modolo, D. (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/cvpr52688.2022.00950 · Cited by 24
AI Coach: A Motor Skill Training System using Motion Discrepancy Detection
Liao, C.-C., Hwang, D.-H., Wu, E., & Koike, H. (2023) · Augmented Humans International Conference (AHs) · doi:10.1145/3582700.3582710 · Cited by 23
Unsupervised Learning of Video Representations via Dense Trajectory Clustering
Tokmakov, P., Hebert, M., & Schmid, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-66096-3_28 · Cited by 22
Self-citation
Hierarchically Decoupled Spatial-Temporal Contrast for Self-supervised Video Representation Learning
Zhang, Z., & Crandall, D. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00105 · Cited by 23
Unsupervised Few-Shot Action Recognition via Action-Appearance Aligned Meta-Adaptation
Patravali, J., Mittal, G., Yu, Y., Li, F., & Chen, M. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00837 · Cited by 22
Video-Text Representation Learning via Differentiable Weak Temporal Alignment
Ko, D., Choi, J., Ko, J., Noh, S., On, K.-W., Kim, E., & Kim, H. J. (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/cvpr52688.2022.00496 · Cited by 22
Self-supervised contrastive video representation learning for construction equipment activity recognition on limited dataset
Ghelmani, A., & Hammad, A. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.105001 · Cited by 27
Controllable augmentations for video representation learning
Qian, R., Lin, W., See, J., & Li, D. (2024) · Visual Intelligence · doi:10.1007/s44267-023-00034-7 · Cited by 23
Learning to Predict Activity Progress by Self-Supervised Video Alignment
Donahue, G., & Elhamifar, E. (2024) · 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/cvpr52733.2024.01766 · Cited by 27
Unsupervised Learning of Eye Gaze Representation from the Web
Dubey, N., Ghosh, S., & Dhall, A. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8851961 · Cited by 22
Imitation Learning from Video by Leveraging Proprioception
Torabi, F., Warnell, G., & Stone, P. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/497 · Cited by 33
Learning Actionness via Long-Range Temporal Order Verification
Zhukov, D., Alayrac, J.-B., Laptev, I., & Šivic, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58526-6_28 · Cited by 22
Unsupervised anomaly detection with a temporal continuation, confidence-aware VAE-GAN
Xing, Z., Mehmood, O., & Smith, W. A. P. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111699 · Cited by 26
Self-supervised deep metric learning for ancient papyrus fragments retrieval
Pirrone, A., Beurton‐Aimar, M., & Journet, N. (2021) · International Journal on Document Analysis and Recognition (IJDAR) · doi:10.1007/s10032-021-00369-1 · Cited by 24
Modelling Neighbor Relation in Joint Space-Time Graph for Video Correspondence Learning
Zhao, Z., Jin, Y., & Heng, P. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00981 · Cited by 21
Space-Time Crop & Attend: Improving Cross-modal Video Representation Learning
Patrick, M., Huang, P.-Y., Misra, I., Metze, F., Vedaldi, A., Asano, Y. M., & Henriques, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01039 · Cited by 28
Self-citation
SCVRL: Shuffled Contrastive Video Representation Learning
Dorkenwald, M., Xiao, F., Brattoli, B., Tighe, J., & Modolo, D. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00458 · Cited by 22
DANet: Semi-supervised differentiated auxiliaries guided network for video action recognition
Gao, G., Liu, Z., Zhang, G., Li, J., & Qin, A. K. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.11.009 · Cited by 23
Domain Knowledge-Informed Self-supervised Representations for Workout Form Assessment
Parmar, P., Gharat, A., & Rhodin, H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19839-7_7 · Cited by 21
Learning to Anticipate Future with Dynamic Context Removal
Xu, X., Li, Y., & Lu, C. (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/cvpr52688.2022.01240 · Cited by 22
Self-Supervised Video-Based Action Recognition With Disturbances
Lin, W. Y., Ding, X., Huang, Y., & Zeng, H. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3269228 · Cited by 21
Aligning Videos in Space and Time
Purushwalkam, S., Tian, Y., Gupta, S., & Gupta, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58574-7_16 · Cited by 22
Toward Anomaly Behavior Detection as an Edge Network Service Using a Dual-Task Interactive Guided Neural Network
Guo, K., Hu, B., Ma, J., Ren, S., Tao, Z., & Zhang, J. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3015987 · Cited by 21
A feature disentangling approach for person re-identification via self-supervised data augmentation
Chen, F., Wang, N., Tang, J., & Zhu, F. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106939 · Cited by 20
Dual Contrastive Learning for Spatio-temporal Representation
Ding, S., Qian, R., & Xiong, H. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3547783 · Cited by 20
Audio-Visual Contrastive Learning with Temporal Self-Supervision
Jenni, S., Black, A., & Collomosse, J. P. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i7.25967 · Cited by 21
Self-Supervised Video Similarity Learning
Kordopatis-Zilos, G., Tolias, G., Tzelepis, C., Kompatsiaris, I. Y., Patras, I., & Papadopoulos, S. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00504 · Cited by 21
Anticipating Where People will Look Using Adversarial Networks
Zhang, M., Ma, K. T., Lim, J., Zhao, Q., & Feng, J. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2871688 · Cited by 20
4D Generic Video Object Proposals
Ošep, A., Voigtlaender, P., Weber, M. C., Luiten, J., & Leibe, B. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196949 · Cited by 20
Self-Supervised Feature Extraction for 3D Axon Segmentation
Klinghoffer, T., Morales, P., Park, Y.-G., Evans, N. B., Chung, K., & Brattain, L. J. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00497 · Cited by 20
Self-supervised Motion Representation via Scattering Local Motion Cues
Tian, Y., Che, Z., Bao, W., Zhai, G., & Gao, Z. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58568-6_5 · Cited by 20
SMART-vision: survey of modern action recognition techniques in vision
AlShami, A. K., Rabinowitz, R., Lâm, K. N., Shleibik, Y., Mersha, M. A., Boult, T. E., & Kalita, J. K. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20484-5 · Cited by 23
Unsupervised Video Understanding by Reconciliation of Posture Similarities
Milbich, T., Bautista, M. Á., Sutter, E., & Ommer, B. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.471 · Cited by 19
Self-Supervised Motion Perception for Spatiotemporal Representation Learning
Liu, C., Yao, Y., Luo, D., Zhou, Y., & Ye, Q. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3160860 · Cited by 19
BaSSL: Boundary-aware Self-Supervised Learning for Video Scene Segmentation
Mun, J., Shin, M., Han, G., Lee, S., Ha, S., Lee, J., & Kim, E. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26316-3_29 · Cited by 22
Self-supervised representation learning by predicting visual permutations
Zhao, Q., & Dong, J. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106534 · Cited by 19
CoCon: Cooperative-Contrastive Learning
Rai, N., Adeli, E., Lee, K.-H., Gaidon, A., & Niebles, J. C. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00377 · Cited by 18
The Group Loss++: A Deeper Look Into Group Loss for Deep Metric Learning
Elezi, I., Seidenschwarz, J., Wagner, L., Vascon, S., Torcinovich, A., Pelillo, M., & Leal-Taixé, L. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3163846 · Cited by 19
Learning Unsupervised Visual Grounding Through Semantic Self-Supervision
Javed, S. A., Saxena, S., & Gandhi, V. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/112 · Cited by 23
Composable Augmentation Encoding for Video Representation Learning
Sun, C., Nagrani, A., Tian, Y., & Schmid, C. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00871 · Cited by 18
Spatiotemporal Contrastive Learning of Facial Expressions in Videos
Roy, S., & Etemad, A. (2021) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii52823.2021.9597460 · Cited by 18
How Severe Is Benchmark-Sensitivity in Video Self-supervised Learning?
Thoker, F. M., Doughty, H., Bagad, P., & Snoek, C. G. M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19830-4_36 · Cited by 18
MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals
Cai, D., Chen, J., Yang, Y., Liu, T., & Li, Y. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599426 · Cited by 22
Modeling Video as Stochastic Processes for Fine-Grained Video Representation Learning
Zhang, H., Liu, D., Zheng, Q., & Su, B. (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/cvpr52729.2023.00221 · Cited by 18
Self-Supervised Representation Learning from Electroencephalography Signals
Banville, H. J., Albuquerque, I., Hyvärinen, A., Moffat, G., Engemann, D. A., & Gramfort, A. (2019) · Machine learning for signal processing ... · doi:10.1109/mlsp.2019.8918693 · Cited by 78
Large Scale Video Representation Learning via Relational Graph Clustering
Lee, H., Lee, J., Ng, J. Y.-H., & Natsev, 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/cvpr42600.2020.00684 · Cited by 18
View-Invariant, Occlusion-Robust Probabilistic Embedding for Human Pose
Liu, T., Sun, J. J., Zhao, L., Zhao, J., Yuan, L., Wang, Y., Chen, L.-C., Schroff, F., & Adam, H. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01529-w · Cited by 22
Motion-Augmented Self-Training for Video Recognition at Smaller Scale
Gavrilyuk, K., Jain, M., Karmanov, I., & Snoek, C. G. M. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01026 · Cited by 18
MaskSplit: Self-supervised Meta-learning for Few-shot Semantic Segmentation
Amac, M. S., Sencan, A., Baran, O. B., İkizler-Cinbiş, N., & Cinbiş, R. G. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00050 · Cited by 18
Semi-supervised Temporal Action Detection with Proposal-Free Masking
Nag, S., Zhu, X., Song, Y.-Z., & Xiang, T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20062-5_38 · Cited by 18
Recent advances in leveraging human guidance for sequential decision-making tasks
Zhang, R., Torabi, F., Warnell, G., & Stone, P. (2021) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-021-09514-w · Cited by 20
Action recognition based on motion of oriented magnitude patterns and feature selection
Phan, H., Vu, N., Nguyen, V., & Quoy, M. (2018) · IET Computer Vision · doi:10.1049/iet-cvi.2017.0282 · Cited by 17
Recurrent Graph Convolutional Autoencoder for Unsupervised Skeleton-Based Action Recognition
Yao, H., Zhao, S.-J., Xie, C., Ye, K., & Liang, S. (2021) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme51207.2021.9428403 · Cited by 17
Beyond Supervised Learning: A Computer Vision Perspective
Chum, L., Subramanian, A., Balasubramanian, V. N., & Jawahar, C. V. (2019) · Journal of the Indian Institute of Science · doi:10.1007/s41745-019-0099-3 · Cited by 18
Exploiting Temporal Coherence for Self-Supervised One-Shot Video Re-identification
Raychaudhuri, D. S., & Roy–Chowdhury, A. K. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58583-9_16 · Cited by 15
Temporal Representation Learning on Monocular Videos for 3D Human Pose Estimation
Honari, S., Constantin, V., Rhodin, H., Salzmann, M., & Fua, P. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3215307 · Cited by 18
Contextualized Relation Predictive Model for Self-Supervised Group Activity Representation Learning
Zhou, W., Kong, L., Han, Y., Qin, J., & Sun, Z. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3265280 · Cited by 18
HNSSL: Hard Negative-Based Self-Supervised Learning
Zhu, W., Liu, J., & Huang, Y. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00506 · Cited by 17
SNNAuth: Sensor-Based Continuous Authentication on Smartphones Using Spiking Neural Networks
Li, Y., Sun, X.-Y., Yang, Z., & Huang, H.-Y. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3349533 · Cited by 17
Impact of Autotuned Fully Connected Layers on Performance of Self-supervised Models for Image Classification
Kishore, J., & Mukherjee, S. (2024) · Machine Intelligence Research · doi:10.1007/s11633-023-1435-7 · Cited by 16
Generating Music Medleys via Playing Music Puzzle Games
Huang, Y.-S., Chou, S.-Y., & Yang, Y. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11843 · Cited by 16
Tricycle: Audio Representation Learning from Sensor Network Data Using Self-Supervision
Cartwright, M., Cramer, A. L., Salamon, J., & Bello, J. P. (2019) · IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) · doi:10.1109/waspaa.2019.8937265 · Cited by 16
Self-supervised Video Representation Learning with Cross-Stream Prototypical Contrasting
Toering, M., Gatopoulos, I., Stol, M., & Hu, V. T. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00092 · Cited by 16
Self-Supervised Video GANs: Learning for Appearance Consistency and Motion Coherency
Hyun, S., Kim, J., & Heo, J.-P. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01068 · Cited by 16
Normalized Human Pose Features for Human Action Video Alignment
Liu, J., Shi, M., Chen, Q., Fu, H., & Tai, C. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01132 · Cited by 16
Time-based self-supervised learning for Wireless Capsule Endoscopy
Pascual, G., Laiz, P., García, A., Wenzek, H. R., Vitria, J. M., & Seguí, S. (2022) · Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona) · Cited by 16
Learning Local Descriptors by Optimizing the Keypoint-Correspondence Criterion: Applications to Face Matching, Learning From Unlabeled Videos and 3D-Shape Retrieval
Markus, N., Pandzic, I., & Ahlberg, J. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2867270 · Cited by 16
E3 Outlier: a Self-Supervised Framework for Unsupervised Deep Outlier Detection
Wang, S., Zeng, Y., Yu, G. W., Cheng, Z., Liu, X., Zhou, S., Zhu, E., Kloft, M., Yin, J., & Liao, Q. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3188763 · Cited by 16
Self-supervised knowledge distillation for complementary label learning
Liu, J., Li, B., Lei, M., & Shi, Y. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.08.014 · Cited by 15
HealNet - Self-supervised Acute Wound Heal-Stage Classification
Carrión, H., Jafari, M., Yang, H., Isseroff, R. R., Rolandi, M., Gomez, M. M., & Norouzi, N. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-21014-3_46 · Cited by 14
Cross-domain few-shot action recognition with unlabeled videos
Wang, X., Zhang, S., Qing, Z., Lv, Y., Gao, C., & Sang, N. (2023) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2023.103737 · Cited by 17
Knowledge-guided pre-training and fine-tuning: Video representation learning for action recognition
Wang, G., Zhou, Y., He, Z., Lu, K., Feng, Y., Liu, Z., & Wang, G. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.127136 · Cited by 15
Emotion recognition of EEG signals based on contrastive learning graph convolutional model
Zhang, Y., Liao, Y., Chen, W., Zhang, X.-R., & Huang, L.-Y. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad7060 · Cited by 15

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