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Shuffle and Learn: Unsupervised Learning Using Temporal Order Verification
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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
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 501–600 of 777
Bi, S., Hu, Z., Zhao, M., Zhang, H., Di, J., & Sun, Z. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.101941 · Cited by 7
Video Face Clustering With Self-Supervised Representation Learning
Sharma, V., Tapaswi, M., Sarfraz, M. S., & Stiefelhagen, R. (2019) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2019.2947264 · Cited by 8
Pang, B., Li, Y., Zhang, Y., Gao, P., Tang, J., Zha, K., Li, J., & Lu, C. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20100 · Cited by 8
Instance Localization for Self-supervised Detection PretrainingPreprint
Yang, C., Wu, Z., Zhou, B., & Lin, S. C.-F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.08318 · Cited by 8
Video 3D Sampling for Self-supervised Representation LearningPreprint
Li, W., Luo, D., Fang, B., Zhou, Y., & Wang, W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.03578 · Cited by 9
MGAP3: Malware Group Attribution Based on PerceiverIO and Polytype Pre-Training
Sun, Y., Chen, S., Lin, S., Sun, A., Long, S., & Li, Z. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3418958 · Cited by 7
DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentation
Wu, C.-H., Chen, S.-H., Hu, C.-Y., Wu, H.-Y., Chen, K., Chen, Y.-Y., Su, C.-H., Lee, C.-K., & Liu, Y.-L. (2024) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52734.2025.01462 · Cited by 17
Srivastava, S., Roy, D., Cartwright, M., Bello, J. P., & Arora, A. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414287 · Cited by 7
Zhang, M., Wang, J., & Ma, A. J. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20239 · Cited by 7
Contrastive Time Series Anomaly Detection by Temporal Transformations
Li, B., & Müller, E. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191358 · Cited by 8
Self-supervised Representation Learning Using 360° Data
Li, J., Liu, J., Wong, Y., Nishimura, S., & Kankanhalli, M. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3351019 · Cited by 5
Self-supervised video representation learning by maximizing mutual information
Xue, F., Ji, H., Zhang, W., & Cao, Y. (2020) · Signal Processing Image Communication · doi:10.1016/j.image.2020.115967 · Cited by 7
Efficient Few-Shot Classification via Contrastive Pretraining on Web Data
Li, Z., Wang, H., Świstek, T., Yu, E., & Wang, H. (2022) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2022.3169463 · Cited by 7
Self-Supervised Video Representation Learning by Video Incoherence Detection
Cao, H., Xu, Y., Mao, K., Xie, L., Yin, J., See, S. C. W., Xu, Q., & Yang, J. (2023) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2023.3265393 · Cited by 9
Context-Aware Sequence Alignment using 4D Skeletal Augmentation
Kwon, T., Tekin, B., Tang, S., & 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/cvpr52688.2022.00800 · Cited by 11
Solution of wide and micro background bias in contrastive action representation learning
Liu, S., Luo, Z., Li, Y., Wang, Y., Fu, W., & Ding, W. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108244 · Cited by 7
Recurrent Deconvolutional Generative Adversarial Networks with Application to Video Generation
Yu, H., Huang, Y., Pi, L., & Wang, L. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-31723-2_2 · Cited by 7
Informed Prefetching in I/O Bounded Distributed Deep Learning
Ruan, X., & Chen, H. (2021) · IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum · doi:10.1109/ipdpsw52791.2021.00127 · Cited by 7
MaMiCo: Macro-to-Micro Semantic Correspondence for Self-supervised Video Representation Learning
Fang, B., Wu, W., Liu, C., Zhou, Y., He, D., & Wang, W. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3547888 · Cited by 8
Unsupervised representation learning with Laplacian pyramid auto-encoders
Zhao, Q., Li, Z., & Junyu, D. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105851 · Cited by 7
SSLM: Self-Supervised Learning for Medical Diagnosis from MR Video.Preprint
Manna, S., Bhattacharya, S., & Pal, U. (2021) · arXiv (Cornell University) · Cited by 7
Xu, X., Li, Y., & Lu, C. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01850-6 · Cited by 6
Fine-Grained Spatiotemporal Motion Alignment for Contrastive Video Representation Learning
Zhu, M., Lin, X., Dang, R., Liu, C., & Chen, Q. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3611932 · Cited by 8
Unsupervised video-based action recognition using two-stream generative adversarial network
Lin, W. Y., Zeng, H., Zhu, J., Hsia, C., Hou, J., & Ma, K. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-09333-y · Cited by 6
DPPMask: Masked Image Modeling with Determinantal Point Processes
Xu, J., Lin, Z., Zhou, D., Yang, Y., Liao, X., Wang, Q., Wu, B., Chen, G., & Heng, P. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00226 · Cited by 7
Video Prediction with Bidirectional Constraint Network
Hou, R., Chang, H., Ma, B., & Chen, X. (2019) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2019.8756585 · Cited by 6
Deep learning models based on automatic labeling with application in echocardiography
Danu, M. D., Ciusdel, C. F., & Lucian Mihai, I. T. U. (2020) · International Conference on System Theory, Control and Computing (ICSTCC) · doi:10.1109/icstcc50638.2020.9259701 · Cited by 6
Self-Supervised Video Action Localization with Adversarial Temporal Transforms
Gong, G., Zheng, L., Jiang, W., & Mu, Y. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/96 · Cited by 6
Video Representation Learning with Graph Contrastive Augmentation
Zhang, J., Xu, X., Shen, F., Yao, Y., Shao, J., & Zhu, X. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475510 · Cited by 6
Leveraging Test-Time Consensus Prediction for Robustness against Unseen Noise
Sarkar, A., Sarkar, A., & Balasubramanian, V. N. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00362 · Cited by 6
Unsupervised Learning of Spatio-Temporal Representation with Multi-Task Learning for Video Retrieval
Kumar, V. (2022) · National Conference on Communications · doi:10.1109/ncc55593.2022.9806811 · Cited by 6
Self Supervised Learning for Multiple Object Tracking in 3D Point Clouds
Kumar, A., Kini, J., Mian, A. S., & Shah, M. A. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981793 · Cited by 6
Action identification with fusion of BERT and 3DCNN for smart home systems
Lê, T. H., Le, T. M., & Nguyen, T. A. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100811 · Cited by 7
Self-Supervised Video Representation Learning by Serial Restoration With Elastic Complexity
Chen, Z., Wang, H., & Chen, C. W. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3293727 · Cited by 6
Automatic classification of heart failure based on Cine-CMR images
Xie, Y., Zhong, H., Wu, J., Zhao, W., Hou, R., Zhao, L., Xu, X., Zhang, M., & Zhao, J. (2023) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-023-03028-4 · Cited by 6
Self-supervised learning-leveraged boosting ultrasound image segmentation via mask reconstruction
Sang, Q., Hou, Y., Qian, P., & Wu, Q. (2023) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-023-02014-1 · Cited by 6
Representation learning from videos in-the-wild: An object-centric approach
Romijnders, R., Mahendran, A., Tschannen, M., Djolonga, J., Ritter, M., Houlsby, N., & Lučić, M. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00022 · Cited by 6
Cascaded Siamese Self-supervised Audio to Video GAN
Aldausari, N., Sowmya, A., Marcus, N., & Mohammadi, G. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00515 · Cited by 6
Self-supervised Learning for Unintentional Action Prediction
Zatsarynna, O., Farha, Y. A., & Gall, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16788-1_26 · Cited by 6
Adversarial Self-Supervised Learning for Semi-Supervised 3D Action RecognitionPreprint
Si, C., Nie, X., Wang, W., Wang, L., Tan, T., & Feng, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.05934 · Cited by 6
Action-Based Representation Learning for Autonomous DrivingPreprint
Xiao, Y., Codevilla, F., Pal, C., & López, A. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.09417 · Cited by 6
Low-Fidelity End-to-End Video Encoder Pre-training for Temporal Action LocalizationPreprint
Xu, M., Pérez-Rúa, J.-M., Zhu, X., Ghanem, B., & Martínez, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.15233 · Cited by 6
Pretext-Contrastive Learning: Toward Good Practices in Self-supervised Video Representation LeaningPreprint
Li, T., Wang, X., & Yamasaki, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.15464 · Cited by 12
There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement LearningPreprint
Grinsztajn, N., Ferret, J., Pietquin, O., Preux, P., & Geist, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.04480 · Cited by 9
Information theory-guided heuristic progressive multi-view coding
Li, J., Gao, H., Qiang, W., & Zheng, C. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.08.027 · Cited by 5
Self-supervised temporal autoencoder for egocentric action segmentation
Zhang, M., Liu, D., Hu, S., Yan, X., Sun, Z., & Ye, Y. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107092 · Cited by 5
SIGMA: Sinkhorn-Guided Masked Video Modeling
Salehi, M., Dorkenwald, M., Thoker, F. M., Gavves, E., Snoek, C. G. M., & Asano, Y. M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72691-0_17 · Cited by 7
Spatio-temporal masked autoencoder-based phonetic segments classification from ultrasound
Dan, X., Xu, K., Zhou, Y., Yang, C., Chen, Y., Dou, Y., & Yang, C. (2025) · Speech Communication · doi:10.1016/j.specom.2025.103186 · Cited by 6
DeepImSeq: Deep image sequencing for unsynchronized cameras
Kanojia, G., & Raman, S. (2018) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.11.014 · Cited by 5
Exploiting Motion Information from Unlabeled Videos for Static Image Action Recognition
Zhang, Y., Niu, L., Pan, Z., Luo, M., Zhang, J., Cheng, D., & Zhang, L. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6990 · Cited by 6
Semi-supervised Facial Action Unit Intensity Estimation with Contrastive Learning
Sánchez, E., Bulat, A., Zaganidis, A., & Tzimiropoulos, G. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69541-5_7 · Cited by 5
Zhong, H., Wu, J., Zhao, W., Xu, X., Hou, R., Zhao, L., Deng, Z., Zhang, M., & Zhao, J. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9630228 · Cited by 5
Learning self-supervised task progression metrics: a case of cloth folding
Verleysen, A., Biondina, M., & wyffels, F. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03466-8 · Cited by 5
Nojehdeh, M. E., & Altun, M. (2023) · Circuits Systems and Signal Processing · doi:10.1007/s00034-023-02363-w · Cited by 5
SelfSeg: A Self-supervised Sub-word Segmentation Method for Neural Machine Translation
Song, H., Dabre, R., Chu, C., Kurohashi, S., & Sumita, E. (2023) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3610611 · Cited by 6
Neural Transformation Network to Generate Diverse Views for Contrastive Learning
Kim, T., Das, D., Choi, S., Jeong, M., Yang, S., Yun, S., & Kim, C. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00518 · Cited by 5
Self-supervised Dense Representation Learning for Live-Cell Microscopy with Time Arrow Prediction
Gallusser, B., Stieber, M., & WEIGERT, M. G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43993-3_52 · Cited by 5
Human Action Classification Using Temporal Slicing for Deep Convolutional Neural Networks
Henderson, N., & Aygün, R. S. (2017) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2017.22 · Cited by 5
OCEAN: Object-centric arranging network for self-supervised visual representations learning
Oh, C., Ham, B., Kim, H., Hilton, A., & Sohn, K. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.01.073 · Cited by 5
Self-Supervised Pre-Training with Acoustic Configurations for Replay Spoofing Detection
Shim, H.-J., Heo, H.-S., Jung, J.-W., & Yu, H.-J. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-1345 · Cited by 5
What My Motion tells me about Your Pose: A Self-Supervised Monocular 3D Vehicle Detector
Picron, C., Chakravarty, P., Roussel, T., & Tuytelaars, T. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9562086 · Cited by 5
Dadashzadeh, A., Whone, A. L., & Mirmehdi, M. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00468 · Cited by 5
GOCA: Guided Online Cluster Assignment for Self-supervised Video Representation Learning
Coskun, H., Zareian, A., Moore, J. L., Tombari, F., & Wang, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19821-2_1 · Cited by 5
Playful Interactions for Representation Learning
Young, S., Pari, J., Abbeel, P., & Pinto, L. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981307 · Cited by 8
Counting Out Time: Class Agnostic Video Repetition Counting in the WildPreprint
Dwibedi, D., Aytar, Y., Tompson, J., Sermanet, P., & Zisserman, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.15418 · Cited by 7
Slot Contrastive Networks: A Contrastive Approach for Representing ObjectsPreprint
Racah, E., & Chandar, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.09294 · Cited by 5
Yang, X., Mirmehdi, M., & Burghardt, T. (2020) · Bristol Research (University of Bristol) · doi:10.48550/arxiv.2010.07217 · Cited by 5
Unsupervised Activity Segmentation by Joint Representation Learning and Online Clustering.Preprint
Kumar, S., Haresh, S., Ahmed, A., Konin, A., Zia, M. Z., & Tran, Q.-H. (2021) · arXiv (Cornell University) · Cited by 5
$$\hbox {I}^2$$MD: 3D Action Representation Learning with Inter- and Intra-Modal Mutual Distillation
Mao, Y., Deng, J., Zhou, W., Lu, Z., Ouyang, W., & Li, H. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02415-5 · Cited by 7
Temporal DINO: A Self-supervised Video Strategy to Enhance Action Prediction
Teeti, I., Rongali, S. B., Singh, V. K., Bradley, A., Banerjee, B., & Fabio Cuzzolin, A. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00352 · Cited by 4
STCLR: Sparse Temporal Contrastive Learning for Video Representation
Altabrawee, H., & Noor, M. H. M. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129694 · Cited by 4
Bi-GRU-Attention Enhanced Unsupervised Network for Skeleton-Based Action Recognition
Chen, L., Ma, N., & Zhang, G. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-9492-9_108 · Cited by 4
Motion Aware Self-Supervision for Generic Event Boundary Detection
Rai, A. K., Krishna, T., Dietlmeier, J., McGuinness, K., Smeaton, A. F., & O'Connor, N. E. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00275 · Cited by 4
Evolutionary Augmentation Policy Optimization for Self-Supervised Learning
Barrett, N., Sadeghi, Z., & Matwin, S. (2023) · Advances in Artificial Intelligence and Machine Learning · doi:10.54364/aaiml.2023.1167 · Cited by 5
Li, J., Qi, G., Zhang, C., Chen, Y., Tan, Y., Xia, C., & Tian, Y. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612085 · Cited by 8
Learning to Visually Connect Actions and Their Effects
Parmar, P., Peh, E., & Fernando, B. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00151 · Cited by 4
Bootstrap Masked Visual Modeling via Hard Patch Mining
Wang, H., Fan, J., Wang, Y., Song, K., Wang, T., Zhang, X., & Zhang, Z. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3557001 · Cited by 9
Identity-Free Artificial Emotional Intelligence via Micro-Gesture Understanding
Gao, R., Liu, X., Xing, B., Yu, Z., Schuller, B. W., & Kalviainen, H. A. (2026) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3649898 · Cited by 4
Cycle Ynet: Semi-supervised Tracking of 3D Anatomical Landmarks
Lin, J., Zhang, Y., Amadou, A. A., Voigt, I., Mansi, T., & Liao, R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59861-7_60 · Cited by 4
Action Recognition Network Using Stacked Short-Term Deep Features and Bidirectional Moving Average
Ha, J., Shin, J., Park, H., & Paik, J. (2021) · Applied Sciences · doi:10.3390/app11125563 · Cited by 4
Synergic learning for noise-insensitive webly-supervised temporal action localization
Zhang, C., Cao, M., Yang, D., Jiang, J., & Zou, Y. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2021.104247 · Cited by 4
Mobile-Based 3D Modeling: An In-Depth Evaluation for the Application in Indoor Scenarios
Pellegrini, M. D., Orlandi, L., Sevegnani, D., & Conci, N. (2021) · Journal of Imaging · doi:10.3390/jimaging7090167 · Cited by 4
Unsupervised Learning for Wafer Surface Defect Pattern Recognition
Geng, S., Liu, H., Wang, F., Zhao, S., & Liu, H. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-6372-7_32 · Cited by 4
Self-supervised temporal event segmentation inspired by cognitive theories
Mounir, R., Aakur, S. N., & Sarkar, S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-822109-9.00021-7 · Cited by 4
Anomaly Detection Using Spatio-Temporal Context Learned by Video Clip Sorting
Shao, W. Q., Kawakami, R., & Naemura, T. (2022) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2021edp7207 · Cited by 4
Chen, T., Chen, Y., Li, H., Gao, T., Tu, H., & Li, S. (2022) · Sensors · doi:10.3390/s22249943 · Cited by 4
FUSSL: Fuzzy Uncertain Self Supervised Learning
Mohamadi, S., Doretto, G., & Adjeroh, D. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00282 · Cited by 4
A Novel Similarity Based Unsupervised Technique for Training Convolutional Filters
Erkoç, T., & Eskil, M. T. (2023) · IEEE Access · doi:10.1109/access.2023.3277253 · Cited by 4
Inferring Dynamic Representations of Facial Actions from a Still ImagePreprint
Song, S., Sánchez, E., Shen, L., Johnston, A., & Valstar, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.02382 · Cited by 4
Evolving Losses for Unlabeled Video Representation LearningPreprint
Piergiovanni, A., Angelova, A., & Ryoo, M. S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.03248 · Cited by 4
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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
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 ›
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