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Shuffle and Learn: Unsupervised Learning Using Temporal Order Verification
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
Research works printed but not linked: 6
- Bengio, Y., Thibodeau-Laufer, E., Alain, G., Yosinski, J.: Deep generative stochastic networks trainable by backprop. arXiv preprint arXiv:1306.1091 (2013) · arXiv
- Rezende, D.J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models. arXiv preprint arXiv:1401.4082 (2014) · arXiv
- Isola, P., Zoran, D., Krishnan, D., Adelson, E.H.: Learning visual groups from co-occurrences in space and time. arXiv preprint arXiv:1511.06811 (2015) · arXiv
- Srivastava, N., Mansimov, E., Salakhutdinov, R.: Unsupervised learning of video representations using lstms. arXiv preprint arXiv:1502.04681 (2015) · arXiv
- Vondrick, C., Pirsiavash, H., Torralba, A.: Anticipating the future by watching unlabeled video. arXiv preprint arXiv:1504.08023 (2015) · arXiv
- Wang, L., Xiong, Y., Wang, Z., Qiao, Y.: Towards good practices for very deep two-stream convnets. arXiv preprint arXiv:1507.02159 (2015) · arXiv
Cited by
Sorted by citations
Items 401–500 of 777
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Li, J., Qiang, W., Zheng, C., Su, B., Razzak, F., Wen, J.-R., & Xiong, H. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3198746 · Cited by 16
Learning visual language models for video understanding
Yang, A. (2023) · theses.fr (ABES) · doi:10.70675/802872a7z8092z4556za1fdz2c7af45f0e72 · Cited by 26
About Time: Advances, Challenges, and Outlooks of Action Understanding
Stergiou, A., & Poppe, R. W. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02478-4 · Cited by 17
Learning Contrastive Representation for Semantic Correspondence
Xiao, T., Liu, S., Mello, S. D., Yu, Z., Kautz, J., & Yang, M. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01602-y · Cited by 15
Federated Self-supervised Learning for Video Understanding
Rehman, Y. A. U., Gao, Y., Shen, J., de Gusmão, P. P. B., & Lane, N. D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19821-2_29 · Cited by 14
Towards Inadequately Pre-trained Models in Transfer Learning
Deng, A., Li, X., Hu, D., Wang, T., Xiong, H., & Xu, C. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01777 · Cited by 17
Self-Supervised Representation Learning From Temporal Ordering of Automated Driving Sequences
Lang, C., Braun, A., Schillingmann, L., Haug, K., & Valada, A. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3354552 · Cited by 14
Explore Video Clip Order With Self-Supervised and Curriculum Learning for Video Applications
Xiao, J., Li, L., Xu, D., Long, C., Shao, J., Zhang, S., Pu, S., & Zhuang, Y. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3025661 · Cited by 14
Strumming to the Beat: Audio-Conditioned Contrastive Video Textures
Narasimhan, M., Ginosar, S., Owens, A., Efros, A. A., & Darrell, T. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00058 · Cited by 17
Epstein, D., & 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/cvpr46437.2021.01104 · Cited by 14
Towards Improved and Interpretable Action Quality Assessment with Self-Supervised Alignment
Roditakis, K., Makris, A., & Argyros, A. (2021) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/3453892.3461624 · Cited by 14
Heterogeneous Contrastive Learning: Encoding Spatial Information for Compact Visual Representations
Huo, X., Xie, L., Wei, L., Zhang, X., Chen, X., Li, H. L., Yang, Z., Zhou, W., Li, H., & Tian, Q. C. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3115335 · Cited by 17
MaCLR: Motion-Aware Contrastive Learning of Representations for Videos
Xiao, F., Tighe, J., & Modolo, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19833-5_21 · Cited by 20
Self-supervised Modal and View Invariant Feature LearningPreprint
Jing, L., Chen, Y., Zhang, L., He, M., & Tian, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.14169 · Cited by 22
Speech2Action: Cross-Modal Supervision for Action Recognition
Nagrani, A., Sun, C., Ross, D., Sukthankar, R., Schmid, C., & Zisserman, A. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01033 · Cited by 12
Anomaly-aware self-supervised feature learning for weakly supervised video anomaly detection
Yang, Z., Wang, G., Guo, Y., Bao, X., & Huang, D. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104379 · Cited by 13
Contrastive Spatio-Temporal Pretext Learning for Self-Supervised Video Representation
Zhang, Y., Po, L.-M., Xu, X., Liu, M., Wang, Y., Ou, W., Zhao, Y., & Yu, W.-Y. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20248 · Cited by 13
Self-supervised utterance order prediction for emotion recognition in conversations
Jiang, D., Liu, H., Tu, G., Wei, R., & Cambria, E. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127370 · Cited by 13
Solving Masked Jigsaw Puzzles with Diffusion Vision Transformers*
Liu, J., Teshome, W., Ghimire, S., Sznaier, M., & Camps, O. (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/cvpr52733.2024.02171 · Cited by 15
Edge-Based Discovery of Training Data for Machine Learning
Feng, Z., George, S., Harkes, J., Pillai, P. S., Klatzky, R. L., & Satyanarayanan, M. (2018) · IEEE/ACM Symposium on Edge Computing (SEC) · doi:10.1109/sec.2018.00018 · Cited by 13
FX-GAN: Self-Supervised GAN Learning via Feature Exchange
Huang, R., Xu, W., Lee, T., Cherian, A., Wang, Y., & Marks, T. K. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093525 · Cited by 14
Multi-Task Self-Supervised Learning for Vehicle Classification Based on Carrier-Free UWB Radars
Zhu, Y., Chen, S., Li, X., Zhang, S., & Zhu, L. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3196085 · Cited by 13
Consistent Intra-Video Contrastive Learning With Asynchronous Long-Term Memory Bank
Chen, Z., Lin, K.-Y., & Zheng, W. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3207174 · Cited by 13
Iterative, Deep Synthetic Aperture Sonar Image Segmentation
Sun, Y.-C., Gerg, I. D., & Monga, V. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3162420 · Cited by 13
Mutual Suppression Network for Video Prediction using Disentangled FeaturesPreprint
Lee, J., Lee, J., Lee, S., & Yoon, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.04810 · Cited by 16
Mamba-attention: A self-supervised framework for efficient remaining useful life prediction
Han, J., Kwon, Y., & Yoon, H. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.111492 · Cited by 16
Qing, Z., Zhang, S., Huang, Z., Xu, Y., Wang, X., Tang, M., Gao, C., Jin, R., & Sang, N. (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/cvpr52688.2022.01345 · Cited by 12
Unsupervised Multimodal Video-to-Video Translation via Self-Supervised Learning
Liu, K., Gu, S., Romero, A., & Timofte, R. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00107 · Cited by 12
Learning to Navigate Intersections with Unsupervised Driver Trait Inference
Liu, S., Chang, P., Chen, H., Chakraborty, N., & Driggs-Campbell, K. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9811635 · Cited by 14
Kumar, V., Tripathi, V., & Pant, B. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-81462-5_61 · Cited by 12
Self-supervised representation learning for detection of ACL tear injury in knee MR videos
Manna, S., Bhattacharya, S., & Pal, U. (2022) · Pattern Recognition Letters · doi:10.1016/j.patrec.2022.01.008 · Cited by 14
LgNet: A Local-Global Network for Action Recognition and Beyond
Zhou, J., Fu, Z., Huang, Q., Liu, Q., & Wang, Y. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3189253 · Cited by 12
Self-Supervised Audio-Visual Representation Learning with Relaxed Cross-Modal Synchronicity
Sarkar, P., & Etemad, A. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i8.26162 · Cited by 17
Learning Temporal Dynamics from Cycles in Narrated Video
Epstein, D., Wu, J., Schmid, C., & Sun, C. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00151 · Cited by 12
Comparing Learning Methodologies for Self-Supervised Audio-Visual Representation Learning
Terbouche, H., Schoneveld, L., Benson, O., & Othmani, A. (2022) · IEEE Access · doi:10.1109/access.2022.3164745 · Cited by 13
Multigrid Predictive Filter Flow for Unsupervised Learning on VideosPreprint
Kong, S., & Fowlkes, C. C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.01693 · Cited by 15
Self-Supervised Representation Learning for Visual Anomaly DetectionPreprint
Ali, R., Khan, M. U. K., & Kyung, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.09654 · Cited by 19
Self-supervised learning for efficient seismic facies classification
Chikhaoui, K., & Alfarraj, M. (2024) · Geophysics · doi:10.1190/geo2023-0508.1 · Cited by 11
Self-Supervised Anomaly Detection With Neural Transformations
Qiu, C., Kloft, M., Mandt, S., & Rudolph, M. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3519543 · Cited by 17
Uncovering the structure of clinical EEG signals with self-supervised learning
Banville, H. J., Chehab, O., Hyvärinen, A., Engemann, D. A., & Gramfort, A. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abca18 · Cited by 279
Modeling the Compatibility of Stem Tracks to Generate Music Mashups
Huang, J., Wang, J.-C., Smith, J. B. L., Song, X., & Wang, Y. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i1.16092 · Cited by 11
Kumar, V., Tripathi, V., & Pant, B. (2022) · International Journal of Mathematical Engineering and Management Sciences · doi:10.33889/ijmems.2022.7.2.018 · Cited by 11
Unsupervised Video-Based Action Recognition With Imagining Motion and Perceiving Appearance
Lin, W. Y., Liu, X., Zhuang, Y., Ding, X., Tu, X., Huang, Y., & Zeng, H. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3221280 · Cited by 11
Self-Supervised Video Representation Learning via Latent Time Navigation
Yang, D., Wang, Y., Kong, Q., Dantcheva, A., Garattoni, L., Francesca, G., & Brémond, F. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i3.25416 · Cited by 11
FLAVR: flow-free architecture for fast video frame interpolation
Kalluri, T., Pathak, D., Chandraker, M. K., & Tran, D. (2023) · Machine Vision and Applications · doi:10.1007/s00138-023-01433-y · Cited by 11
Tubelet-Contrastive Self-Supervision for Video-Efficient Generalization
Thoker, F. M., Doughty, H., & Snoek, C. G. M. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01270 · Cited by 12
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Wu, H., Lei, C., Sun, X., Wang, P., Chen, Q., Cheng, K.-T. T., Lin, S. C.-F., & Wu, Z. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01494 · Cited by 12
Sustained Self-Supervised Pretraining for Temporal Order Verification
Buckchash, H., & Raman, B. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34869-4_16 · Cited by 11
Self-Supervised Human Activity Recognition by Augmenting Generative Adversarial Networks
Zadeh, M. Z., Babu, A. R., Jaiswal, A., Kyrarini, M., & Makedon, F. (2021) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/3453892.3453893 · Cited by 13
Self-Supervised Learning of Compressed Video Representations
Yu, Y., Lee, S., Kim, G., & Song, Y. (2021) · Seoul National University Open Repository (Seoul National University) · Cited by 12
Transcript to Video: Efficient Clip Sequencing from Texts
Xiong, Y., Heilbron, F. C., & Lin, D. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548268 · Cited by 11
Self-Supervised Object Localization with Joint Graph Partition
Su, Y., Lin, G., Hao, Y., Cao, Y.-W., Wang, W., & Wu, Q. (2022) · AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20127 · Cited by 17
Self-Supervised Learning via Multi-Transformation Classification for Action Recognition
Vu, D.-Q., Le, N., & Wang, J. (2024) · IEEE International Conference on Multimedia and Expo workshops · doi:10.1109/icmew63481.2024.10645477 · Cited by 13
Learning Actionable Representations from Visual ObservationsPreprint
Dwibedi, D., Tompson, J., Lynch, C., & Sermanet, P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.00928 · Cited by 18
Visuomotor Understanding for Representation Learning of Driving ScenesPreprint
Lee, S., Kim, J., Oh, T.-H., Jeong, Y., Yoo, D., Lin, S. C.-F., & Kweon, I. S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.06979 · Cited by 11
Self-supervised Temporal Discriminative Learning for Video Representation LearningPreprint
Wang, J., Lin, Y., Ma, A. J., & Yuen, P. C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.02129 · Cited by 11
Wang, J., Gao, Y., Li, K., Lin, Y., Ma, A. J., Cheng, H., Peng, P., Huang, F., Ji, R., & Sun, X. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.05769 · Cited by 11
Self-supervised patient-specific features learning for OCT image classification
Fang, L.-Y., Guo, J.-H., He, X., & Li, M. (2022) · Medical and Biological Engineering and Computing · doi:10.1007/s11517-022-02627-8 · Cited by 9
ArrowGAN : Learning to generate videos by learning Arrow of Time
Hong, K., Uh, Y., & Byun, H. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.01.043 · Cited by 10
Self-Supervised Enhancement for Named Entity Disambiguation via Multimodal Graph Convolution
Zhou, P., Ying, K., Wang, Z., Guo, D., & Bai, C. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3173179 · Cited by 11
Multimodal Knowledge Learning for Named Entity Disambiguation
Dong-jie, Z., & Huang, L. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.230 · Cited by 10
Transform-Equivariant Consistency Learning for Temporal Sentence Grounding
Liu, D., Qu, X., Dong, J., Zhou, P., Xu, Z., Wang, H., Di, X., Lu, W., & Cheng, Y. (2023) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3634749 · Cited by 10
STEPs: Self-Supervised Key Step Extraction and Localization from Unlabeled Procedural Videos
Shah, A., Lundell, B., Sawhney, H., & Chellappa, R. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00952 · Cited by 10
No More Shortcuts: Realizing the Potential of Temporal Self-Supervision
Dave, I. R., Jenni, S., & Shah, M. A. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i2.27913 · Cited by 11
Biswas, M., Buckchash, H., & Prasad, D. K. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127810 · Cited by 11
Learning Spatiotemporal 3D Convolution with Video Order Self-supervision
Suzuki, T., Itazuri, T., Hara, K., & Kataoka, H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11012-3_45 · Cited by 10
Spatial-Temporal Knowledge Integration: Robust Self-Supervised Facial Landmark Tracking
Zhu, C., Li, X., Li, J., Ding, G., & Tong, W. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413993 · Cited by 10
Towards a self-sufficient face verification system
López‐López, E., Regueiro, C. V., Pardo, X. M., Franco, A., & Lumini, A. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114734 · Cited by 10
Bi-calibration Networks for Weakly-Supervised Video Representation Learning
Long, F., Yao, T., Qiu, Z., Tian, X., Luo, J., & Mei, T. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01779-w · Cited by 10
Similarity contrastive estimation for image and video soft contrastive self-supervised learning
Denize, J., Rabarisoa, J., Orcesi, A., & Hérault, R. (2023) · Machine Vision and Applications · doi:10.1007/s00138-023-01444-9 · Cited by 11
Temporal Contrastive Graph Learning for Video Action Recognition and RetrievalPreprint
Liu, Y., Wang, K., Lan, H., & Lin, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.00820 · Cited by 12
MoDist: Motion Distillation for Self-supervised Video Representation Learning.Preprint
Xiao, F., Tighe, J., & Modolo, D. (2021) · arXiv (Cornell University) · Cited by 9
Video and audio are images: A cross-modal mixer for original data on video–audio retrieval
Yuan, Z., Shen, Q., Zheng, B., Liu, Y., Jiang, L., & Guo, G. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112076 · Cited by 9
Rani, A., Ortíz-Arroyo, D., & Durdevic, P. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108921 · Cited by 10
Efficient Image Pre-training with Siamese Cropped Masked Autoencoders
Eymaël, A., Vandeghen, R., Cioppa, A., Giancola, S., Ghanem, B., & Droogenbroeck, M. V. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73337-6_20 · Cited by 10
Ge, Y., Zhang, H., Shi, J., Luo, D., Chang, S., He, J. W., Huang, Q., & Wang, H. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113090 · Cited by 9
Semantics Meets Temporal Correspondence: Self-supervised Object-centric Learning in Videos
Qian, R., Ding, S., Liu, X., & Lin, D.-H. (2023) · IEEE International Conference on Computer Vision · doi:10.1109/iccv51070.2023.01529 · Cited by 27
Unsupervised Cross-Dataset Adaptation via Probabilistic Amodal 3D Human Pose Completion
Kundu, J. N., Rahul, M., Patravali, J., & Babu, R. V. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093577 · Cited by 9
Introduction to Domain Adaptation
Venkateswara, H., & Panchanathan, S. (2020) · Springer eBooks · doi:10.1007/978-3-030-45529-3_1 · Cited by 10
S4: Self-Supervised Learning of Spatiotemporal Similarity
Tkachev, G., Frey, S., & Ertl, T. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3101418 · Cited by 9
Order-Constrained Representation Learning for Instructional Video Prediction
Li, M., Chen, L., Lu, J., Feng, J., & Zhou, J. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3149329 · Cited by 9
Zhou, J., & Komuro, T. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2022.103491 · Cited by 9
DimCL: Dimensional Contrastive Learning for Improving Self-Supervised Learning
Nguyen, T., Pham, T. X., Zhang, C., Luu, T. M., Vu, T., & Yoo, C. D. (2023) · IEEE Access · doi:10.1109/access.2023.3236087 · Cited by 13
Semantics Guided Contrastive Learning of Transformers for Zero-shot Temporal Activity Detection
Nag, S., Goldstein, O., & Roy–Chowdhury, A. K. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00618 · Cited by 9
Hadikhani, P., Lai, D. T. C., & Ong, W.-H. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3266603 · Cited by 9
Order Matters: Shuffling Sequence Generation for Video PredictionPreprint
Wang, J., Hu, B., Long, Y., & Guan, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.08845 · Cited by 9
Human Mesh Recovery from Monocular Images via a Skeleton-disentangled RepresentationPreprint
Sun, Y. M., Ye, Y., Liu, W., Wenpeng, G., Fu, Y., & Mei, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.07172 · Cited by 9
Self-supervised Representation Learning with Relative Predictive CodingPreprint
Tsai, Y.-H. H., Ma, M. Q., Yang, M., Zhao, H. Q., Morency, L., & Salakhutdinov, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.11275 · Cited by 23
Enhancing Unsupervised Video Representation Learning by Temporal Contrastive Modelling Using 2D CNN
Kumar, V., Tripathi, V., & Pant, B. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-11349-9_43 · Cited by 8
Self-Supervised Representation Learning for Knee Injury Diagnosis From Magnetic Resonance Data
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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
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