Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition
Open accessIntegrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Existing contrastive learning methods for skeleton-based action recognition use normal augmentations to construct similar positive samples, which limits the ability to explore novel movement patterns.
“The existing contrastive learning methods use normal augmentations to construct similar positive samples, which limits the ability to explore novel movement patterns.”
From Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-Supervised Action Recognition (2022) · cites this paper as Rao et al. 2021
For a single joint stream on NTU-60, AimCLR outperforms all other methods.
“As shown in Table 4, for a single stream (i.e., joint stream), our AimCLR outperforms all other methods (Zheng et al. 2018; Lin et al. 2020; Rao et al. 2021; Su, Liu, and Shlizerman 2020; Nie, Liu, and Liu 2020; Li et al. 2021).”
From Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-Supervised Action Recognition (2022) · cites this paper as Rao et al. 2021
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 · see citing works
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545 · see citing works
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,100 · see citing works
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,268 · see citing works
Momentum Contrast for Unsupervised Visual Representation Learning
He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00975 · Cited by 13,347 · see citing works
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020) · arXiv · doi:10.48550/arxiv.2002.05709 · Cited by 10,857 · see citing works
Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390294 · Cited by 7,436 · see citing works
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
Yan, S., Xiong, Y., & Lin, D. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12328 · Cited by 5,410 · cited 5× · see citing works
“Most existing skeleton-based methods [9]–[11] utilize the supervised learning paradigm to learn action representations, in which a significant number of annotations for action frames or videos is indispensable.”
BackgroundMethodInfluentialDimensionality Reduction by Learning an Invariant Mapping
Hadsell, R., Chopra, S., & LeCun, Y. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.100 · Cited by 5,336 · see citing works
Unsupervised Feature Learning via Non-parametric Instance Discrimination
Wu, Z., Xiong, Y., Yu, S. X., & Lin, D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00393 · Cited by 3,664 · see citing works
Zhang, R., Isola, P., & Efros, A. A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46487-9_40 · Cited by 3,361 · see citing works
NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
Shahroudy, A., Liu, J., Ng, T.-T., & Wang, G. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.115 · Cited by 3,148 · see citing works
Learning deep representations by mutual information estimation and maximization
Hjelm, R. D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Bachman, P., Trischler, A., & Bengio, Y. (2018) · arXiv · doi:10.48550/arxiv.1808.06670 · Cited by 1,991 · see citing works
Hierarchical recurrent neural network for skeleton based action recognition
Du, Y., Wang, W., & Wang, L. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298714 · Cited by 1,962 · see citing works
Unsupervised Learning of Video Representations using LSTMs
Srivastava, N., Mansimov, E., & Salakhutdinov, R. (2015) · arXiv · doi:10.48550/arxiv.1502.04681 · Cited by 1,861 · see citing works
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
Liu, J., Shahroudy, A., Pérez, M., Wang, G., Duan, L., & Kot, A. C. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2916873 · Cited by 1,841 · see citing works
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Gutmann, M. U., & Hyvärinen, A. (2010) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 1,588 · see citing works
Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group
Vemulapalli, R., Arrate, F., & Chellappa, R. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.82 · Cited by 1,506 · see citing works
Big Self-Supervised Models are Strong Semi-Supervised LearnersPreprint
Chen, T., Kornblith, S., Swersky, K., Norouzi, M., & Hinton, G. E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.10029 · Cited by 1,480 · see citing works
An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition
Si, C., Chen, W., Wang, W., Wang, L., & Tan, T. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00132 · Cited by 973 · see citing works
HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences
Oreifej, O., & Liu, Z. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.98 · Cited by 950 · see citing works
Shuffle and Learn: Unsupervised Learning Using Temporal Order Verification
Misra, I., Zitnick, C. L., & Hebert, M. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_32 · Cited by 779 · see citing works
Unsupervised Embedding Learning via Invariant and Spreading Instance Feature
Ye, M., Zhang, X., Yuen, P. C., & Chang, S. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00637 · Cited by 629 · see citing works
Modeling video evolution for action recognition
Fernando, B., Gavves, E., Jose Oramas, M., Ghodrati, A., & Tuytelaars, T. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299176 · Cited by 573 · see citing works
Two-person interaction detection using body-pose features and multiple instance learning
Yun, K., Honorio, J., Chattopadhyay, D., Berg, T. L., & Samaras, D. (2012) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2012.6239234 · Cited by 539 · see citing works
Ghahramani, Z. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-28650-9_5 · Cited by 476 · see citing works
Wang, H., & Wang, L. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.387 · Cited by 471 · see citing works
Super Normal Vector for Activity Recognition Using Depth Sequences
Yang, X., & Tian, Y. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.108 · Cited by 393 · see citing works
Challenges in real-life emotion annotation and machine learning based detection
Devillers, L. Y., Vidrascu, L., & Lamel, L. F. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.03.007 · Cited by 341 · see citing works
Joint Angles Similarities and HOG2 for Action Recognition
Ohn-Bar, E., & Trivedi, M. M. (2013) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2013.76 · Cited by 321 · see citing works
Skeletal Quads: Human Action Recognition Using Joint Quadruples
Evangelidis, G., Singh, G., & Horaud, R. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.772 · Cited by 279 · see citing works
A Comparative Review of Recent Kinect-Based Action Recognition Algorithms
Wang, L., Huynh, D. Q., & Koniusz, P. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2925285 · Cited by 261 · see citing works
PREDICT & CLUSTER: Unsupervised Skeleton Based Action Recognition
Su, K., Liu, X., & Shlizerman, E. (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/cvpr42600.2020.00965 · Cited by 244 · see citing works
MS2L: Multi-Task Self-Supervised Learning for Skeleton Based Action Recognition
Lin, L., Song, S., Yang, W., & Liu, J. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413548 · Cited by 223 · see citing works
Unsupervised Representation Learning With Long-Term Dynamics for Skeleton Based Action Recognition
Zheng, N., Wen, J., Liu, R., Long, L., Dai, J., & Gong, Z. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11853 · Cited by 201 · see citing works
HoloGAN: Unsupervised Learning of 3D Representations From Natural Images
Nguyen-Phuoc, T., Li, C., Theis, L., Richardt, C., & Yang, Y. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00255 · Cited by 197 · see citing works
HOPC: Histogram of Oriented Principal Components of 3D Pointclouds for Action Recognition
Rahmani, H., Mahmood, A., Huynh, D. Q., & Mian, A. S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10605-2_48 · Cited by 182 · see citing works
Early Action Prediction by Soft Regression
Hu, J., Zheng, W., Ma, L., Wang, G., Lai, J., & Zhang, J. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2863279 · Cited by 124 · see citing works
Video Jigsaw: Unsupervised Learning of Spatiotemporal Context for Video Action Recognition
Ahsan, U., Madhok, R., & Essa, I. A. (2019) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2019.00025 · Cited by 113 · see citing works
Unsupervised Learning of View-invariant Action RepresentationsPreprint
Li, J., Wong, Y., Zhao, Q., & Kankanhalli, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.01844 · Cited by 95 · see citing works
Attention-Based Multilevel Co-Occurrence Graph Convolutional LSTM for 3-D Action Recognition
Xu, S., Rao, H., Peng, H., Jiang, X., Guo, Y., Hu, X., & Hu, B. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3042986 · Cited by 45 · see citing works
Self-citationPREDICT & CLUSTER: Unsupervised Skeleton Based Action RecognitionPreprint
Su, K., Liu, X., & Shlizerman, E. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.12409 · Cited by 40 · see citing works
Unsupervised Hierarchical Dynamic Parsing and Encoding for Action Recognition
Su, B., Zhou, J., Ding, X., & Wu, Y. N. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2745212 · Cited by 20 · see citing works
Flagged first, then by citations
13 of the 57 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
A multi-modal open dataset for mental-disorder analysis
Cai, H., Yuan, Z., Gao, Y., Sun, S., Li, N., Tian, F., Xiao, H., Li, J., Yang, Z., Li, X., et al. (2022) · Scientific Data · doi:10.1038/s41597-022-01211-x · Cited by 293 · see citing works
Self-citation3D Human Action Representation Learning via Cross-View Consistency Pursuit
Li, L., Wang, M., Ni, B., Wang, H., Yang, J., & Zhang, W. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00471 · Cited by 236 · see citing works
“As shown in Table 6, TSRJI [2] supervisedly utilizes attention LSTM, ASCAL [39] adopts LSTM for skeleton modeling, and our method defeats the other unsupervised method and some of the supervised methods.”
BackgroundMethodInfluentialKumar, P., Chauhan, S., & Awasthi, L. K. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-09986-x · Cited by 220 · see citing works
Amiri, Z., Heidari, A., Navimipour, N. J., Ünal, M., & Mousavi, A. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16382-x · Cited by 210 · see citing works
Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., & Ding, R. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i1.19957 · Cited by 181 · see citing works
“Different from traditional contrastive learning methods (Rao et al. 2021; Li et al. 2021) which directly use normally augmented view, a novel framework proposed in our work is based on the extreme augmentations and the drop mechanism which obtain diverse positive samples and bring abundant…”
BackgroundMethodInfluentialShu, X., Xu, B., Zhang, L., & Tang, J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3222871 · Cited by 159 · see citing works
Sequence-to-Sequence Contrastive Learning for Text Recognition
Aberdam, A., Litman, R., Tsiper, S., Anschel, O., Slossberg, R., Mazor, S., Manmatha, R., & Perona, P. (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/cvpr46437.2021.01505 · Cited by 149 · see citing works
GraphGST: Graph Generative Structure-Aware Transformer for Hyperspectral Image Classification
Jiang, M., Su, Y., Gao, L., Plaza, A., Zhao, X.-L., Sun, X., & Liu, G. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3349076 · Cited by 135 · see citing works
Transformer for Skeleton-based action recognition: A review of recent advances
Xin, W., Liu, R.-Y., Liu, Y., Chen, Y., Yu, W., & Miao, Q. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.03.001 · Cited by 117 · see citing works
Xu, B., Shu, X., & Song, Y. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3175605 · Cited by 102 · see citing works
Xu, B., Shu, X., Zhang, J., Dai, G., & Song, Y. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3247103 · Cited by 100 · see citing works
Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition
Lin, L., Zhang, J., & Liu, J. (2023) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52729.2023.00234 · Cited by 120 · see citing works
Li, J., Chen, J., Kong, W., Li, X., & Hu, B. (2021) · Journal of Affective Disorders · doi:10.1016/j.jad.2021.09.074 · Cited by 85 · see citing works
Self-citationMore than Encoder: Introducing Transformer Decoder to Upsample
Li, Y., Cai, W., Gao, Y., Li, C., & Hu, X. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995378 · Cited by 75 · see citing works
Self-citationGlobal-Local Motion Transformer for Unsupervised Skeleton-Based Action Learning
Kim, B., Chang, H. J., Kim, J., & Choi, J. Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19772-7_13 · Cited by 72 · see citing works
Zhang, J., Lin, L., & Liu, J. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i3.25451 · Cited by 71 · see citing works
Masked Motion Predictors are Strong 3D Action Representation Learners
Mao, Y., Deng, J., Zhou, W., Yao, F., Ouyang, W., & Li, H. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00934 · Cited by 71 · see citing works
Prototypical Contrast and Reverse Prediction: Unsupervised Skeleton Based Action Recognition
Xu, S., Rao, H., Hu, X., Cheng, J., & Hu, B. (2020) · IEEE transactions on multimedia · doi:10.1109/tmm.2021.3129616 · Cited by 77 · see citing works
Self-citationPRA-Net: Part-and-Relation Attention Network for depression recognition from facial expression
Liu, Z., Yuan, X., Li, Y., Shangguan, Z., Zhou, L., & Hu, B. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106589 · Cited by 69 · see citing works
Self-citationWang, P., Wen, J., Si, C., Qian, Y., & Wang, L. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3207577 · Cited by 69 · see citing works
Hierarchical Contrast for Unsupervised Skeleton-Based Action Representation Learning
Dong, J., Sun, S., Liu, Z., Chen, S., Liu, B., & Wang, X. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i1.25127 · Cited by 66 · see citing works
SkeletonMAE: Graph-based Masked Autoencoder for Skeleton Sequence Pre-training
Yan, H., Liu, Y., Wei, Y., Li, Z., Li, G., & Lin, L. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00516 · Cited by 65 · see citing works
Rao, H., & Miao, C. (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/cvpr52729.2023.02118 · Cited by 61 · see citing works
Self-citationCMD: Self-supervised 3D Action Representation Learning with Cross-Modal Mutual Distillation
Mao, Y., Zhou, W., Lu, Z., Deng, J., & Li, H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20062-5_42 · Cited by 61 · see citing works
Efficient Spatio-Temporal Contrastive Learning for Skeleton-Based 3-D Action Recognition
Gao, X., Yang, Y., Zhang, Y., Li, M., Yu, J.-G., & Du, S. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3127040 · Cited by 59 · see citing works
Self-supervised Action Representation Learning from Partial Spatio-Temporal Skeleton Sequences
Zhou, Y., Duan, H., Rao, A., Su, B., & Wang, J. (2023) · AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i3.25495 · Cited by 79 · see citing works
Digital Phenotyping and Feature Extraction on Smartphone Data for Depression Detection
Yang, M., Ngai, E. C. ‐., Hu, X., Hu, B., Liu, J., Erol Gelenbe, A., & Leung, V. C. M. (2024) · Proceedings of the IEEE · doi:10.1109/jproc.2025.3542324 · Cited by 54 · see citing works
Self-citationContrastive Positive Mining for Unsupervised 3D Action Representation Learning
Zhang, H., Hou, Y., Zhang, W., & Li, W. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19772-7_3 · Cited by 51 · see citing works
Skeleton-Based Action Recognition Through Contrasting Two-Stream Spatial-Temporal Networks
Pang, C., Lu, X., & Lyu, L. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3239751 · Cited by 51 · see citing works
Liu, H., Wang, B., Bao, Z., Xue, M.-B., Kang, S., Jiang, D., Liu, Y.-S., & Ren, B. (2022) · AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20062 · Cited by 50 · see citing works
Shao, X., Sun, S., Li, J., Kong, W., Zhu, J., Li, X., & Hu, B. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3092140 · Cited by 47 · see citing works
Self-citationIIP-Transformer: Intra-Inter-Part Transformer for Skeleton-Based Action Recognition
Wang, Q., Shi, S., He, J., Peng, J., Liu, T., & Weng, R. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386970 · Cited by 50 · see citing works
3D Human Action Recognition: Through the eyes of researchers
Sarkar, A., Banerjee, A., Singh, P. K., & Sarkar, R. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116424 · Cited by 43 · see citing works
Motion Guided Attention Learning for Self-Supervised 3D Human Action Recognition
Yang, Y., Liu, G., & Gao, X. (2022) · IEEE transactions on circuits and systems for video technology (Print) · doi:10.1109/tcsvt.2022.3194350 · Cited by 36 · see citing works
Liu, M., Meng, F., & Liang, Y. (2022) · Cyborg and Bionic Systems · doi:10.34133/cbsystems.0002 · Cited by 39 · see citing works
HaLP: Hallucinating Latent Positives for Skeleton-based Self-Supervised Learning of Actions
Shah, A. B., Roy, A., Shah, K., Mishra, S. K., Jacobs, D., Cherian, A., & Chellappa, R. (2023) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52729.2023.01807 · Cited by 56 · see citing works
Wang, M., Li, X., Chen, S., Zhang, X., Ma, L., & Zhang, Y. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3307933 · Cited by 37 · see citing works
Modeling the Relative Visual Tempo for Self-supervised Skeleton-based Action Recognition
Zhu, Y., Han, H., Yu, Z., & Liu, G. (2023) · IEEE International Conference on Computer Vision · doi:10.1109/iccv51070.2023.01279 · Cited by 41 · see citing works
Unified Multi-modal Unsupervised Representation Learning for Skeleton-based Action Understanding
Sun, S., Liu, D., Dong, J., Qu, X., Gao, J., Yang, X., Wang, X., & Wang, M. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612449 · Cited by 35 · see citing works
Chu, X., Kim, S., Cho, Y., & Park, K.-S. (2024) · Electronics · doi:10.3390/electronics13040747 · Cited by 37 · see citing works
Self-Supervised 3D Action Representation Learning With Skeleton Cloud Colorization
Yang, S., Liu, J., Lu, S., Er, M. H., Hu, Y., & Kot, A. C. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3325463 · Cited by 35 · see citing works
SSRL: Self-Supervised Spatial-Temporal Representation Learning for 3D Action Recognition
Jin, Z., Wang, Y., Wang, Q.-C., Shen, Y.-H., & Meng, H.-Y. (2024) · IEEE transactions on circuits and systems for video technology (Print) · doi:10.1109/tcsvt.2023.3284493 · Cited by 26 · see citing works
Improving self-supervised action recognition from extremely augmented skeleton sequences
Guo, T.-Y., Liu, M.-Y., Liu, H., Wang, G.-Q., & Li, W.-H. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110333 · Cited by 31 · see citing works
Focalized contrastive view-invariant learning for self-supervised skeleton-based action recognition
Men, Q., Ho, E. S. L., Shum, H. P. H., & Leung, H. W. H. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.03.070 · Cited by 28 · see citing works
QCLR: Quantum-LSTM contrastive learning framework for continuous mental health monitoring
Padha, A., & Sahoo, A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121921 · Cited by 28 · see citing works
Liu, R.-Y., Liu, Y., Wu, M., Xin, W., Miao, Q.-G., Liu, X.-Z., & Li, L. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111377 · Cited by 36 · see citing works
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 · see citing works
See Your Emotion from Gait Using Unlabeled Skeleton Data
Lu, H., Hu, X., & Hu, B. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i2.25272 · Cited by 25 · see citing works
Self-citationHierarchical Aggregated Graph Neural Network for Skeleton-Based Action Recognition
Geng, P., Lu, X., Li, W., & Lyu, L. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3428330 · Cited by 25 · see citing works
Multimodal Emotion Recognition with Modality-Pairwise Unsupervised Contrastive Loss
Franceschini, R., Fini, E., Beyan, C., Conti, A., Arrigoni, F., & Ricci, E. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956589 · Cited by 27 · see citing works
DMMG: Dual Min-Max Games for Self-Supervised Skeleton-Based Action Recognition
Guan, S., Yu, X., Huang, W., Fang, G., & Lu, H. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3338410 · Cited by 23 · see citing works
Enhanced detection and classification of microplastics in marine environments using deep learning
Akkajit, P., Alahi, M. E. E., & Sukkuea, A. (2024) · Regional Studies in Marine Science · doi:10.1016/j.rsma.2024.103880 · Cited by 22 · see citing works
Mutual Information Driven Equivariant Contrastive Learning for 3D Action Representation Learning
Lin, L., Zhang, J., & Liu, J. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3372451 · Cited by 21 · see citing works
Robust 2D Skeleton Action Recognition via Decoupling and Distilling 3D Latent Features
Zhang, X., Jia, Y., Zhang, J., Yang, Y., & Tu, Z. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3562877 · Cited by 21 · see citing works
Qiu, H., Wu, Y., Duan, M., & Jin, C. (2022) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme52920.2022.9859752 · Cited by 20 · see citing works
Momentum Cross-Modal Contrastive Learning for Video Moment Retrieval
Han, D. G., Cheng, X., Guo, N., Ye, X., Rainer, B., & Priller, P. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3344097 · Cited by 20 · see citing works
A survey of artificial intelligence in gait-based neurodegenerative disease diagnosis
Rao, H., Zeng, M., Zhao, X., & Miao, C. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129533 · Cited by 24 · see citing works
Self-citationRao, H., & Miao, C. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/180 · Cited by 20 · see citing works
Self-citationA transformer-based network for perceptual contrastive underwater image enhancement
Cheng, N., Sun, Z., Zhu, X., & Wang, H. (2023) · Signal Processing Image Communication · doi:10.1016/j.image.2023.117032 · Cited by 19 · see citing works
Zeng, Q., Liu, C., Liu, M., & Chen, Q. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3253048 · Cited by 18 · see citing works
Cross-stream contrastive learning for self-supervised skeleton-based action recognition
Li, D., Tang, Y., Zhang, Z., & Zhang, W. (2023) · Image and Vision Computing · doi:10.1016/j.imavis.2023.104689 · Cited by 18 · see citing works
Karlov, M., Abedi, A., & Khan, S. S. (2024) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-024-03177-x · Cited by 21 · see citing works
How and What to Learn: Taxonomizing Self-Supervised Learning for 3D Action Recognition
Tanfous, A. B., Zerroug, A., Linsley, D. A., & Serre, T. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00294 · Cited by 17 · see citing works
Augmented skeleton sequences with hypergraph network for self-supervised group activity recognition
Wang, G., Liu, M., Liu, H., Guo, P., Wang, T., Guo, J., & Fan, R. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110478 · Cited by 17 · see citing works
Wang, Y., Xia, Y., & Liu, S. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.048813 · Cited by 16 · see citing works
Localized Linear Temporal Dynamics for Self-Supervised Skeleton Action Recognition
Wang, X., & Mu, Y. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3405712 · Cited by 16 · see citing works
Bootstrapped Representation Learning for Skeleton-Based Action Recognition
Moliner, O., Huang, S., & Åström, K. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00460 · Cited by 17 · see citing works
A Systematic Review of Skeleton-Based Action Recognition: Methods, Challenges, and Future Directions
Liu, Y., Liu, R., Hu, Y., Wu, M., Xin, W., Miao, Q., Wu, S., & Li, L. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3632689 · Cited by 15 · see citing works
Combining Supervised and Unsupervised Learning Algorithms for Human Activity Recognition
Budisteanu, E.-A., & Mocanu, I. G. (2021) · Sensors · doi:10.3390/s21186309 · Cited by 15 · see citing works
Supervised Contrastive Learning for Vehicle Classification Based on the IR-UWB Radar
Li, X., Zhang, S., Zhu, Y., Xiao, Z., & Chen, S. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3203468 · Cited by 15 · see citing works
A GPS-Adaptive Spoofing Detection Method for the Small UAV Cluster
Meng, L., Zhang, L., Yang, L., & Yang, W. (2023) · Drones · doi:10.3390/drones7070461 · Cited by 14 · see citing works
MacDiff: Unified Skeleton Modeling with Masked Conditional Diffusion
Wu, L., Lin, L., Zhang, J., Ma, Y., & Liu, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73347-5_7 · Cited by 14 · see citing works
Global–local contrastive multiview representation learning for skeleton-based action recognition
Bian, C., Feng, W., Meng, F., & Wang, S. (2023) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2023.103655 · Cited by 14 · see citing works
TSFF: A Triple-Stream Feature Fusion Method for Ethereum Phishing Scam Detection
Hou, W., Cui, B., Chen, Y., Li, R., & Song, W. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3473771 · Cited by 13 · see citing works
Lerch, D. J., Zhong, Z., Martín, M., Voit, M., & Beyerer, J. (2024) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw60836.2024.00027 · Cited by 12 · see citing works
Li, C., Li, S., Gao, Y., Gao, X., Chen, P., Li, J., & Li, W. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3540292 · Cited by 12 · see citing works
Filterable sample consensus based on angle variance for pupil segmentation
Zhong, J., Wang, D., Wu, H., Wang, P., Yang, M., Peng, H., & Hu, B. (2022) · Digital Signal Processing · doi:10.1016/j.dsp.2022.103695 · Cited by 12 · see citing works
Self-citationDai, C., Wei, Y., Xu, Z., Chen, M.-S., Liu, Y., & Fan, J.-L. (2023) · Italian National Conference on Sensors · doi:10.3390/s23052452 · Cited by 9 · see citing works
Self-Supervised Skeleton Representation Learning Via Actionlet Contrast and Reconstruct
Lin, L., Zhang, J., & Liu, J. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3598138 · Cited by 14 · see citing works
Self-Supervised Skeleton-Based Action Representation Learning: A Benchmark and Beyond
Zhang, J., Lin, L., Yang, S., & Liu, J. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-025-02644-8 · Cited by 25 · see citing works
Multi-stream Global–Local Motion Fusion Network for skeleton-based action recognition
Qi, Y., Pang, C., Liu, Y., & Lyu, L. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110536 · Cited by 11 · see citing works
He, Z., Lv, J., & Fang, S. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127495 · Cited by 11 · see citing works
Xu, J., Liu, F., Wang, Q., Zou, R., Wang, Y., Zheng, J., Du, S., & Zeng, W. (2024) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-024-01156-w · Cited by 11 · see citing works
Qi, Y., Hu, J., Han, X., Hu, L., & Zhao, Z. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08814-4 · Cited by 11 · see citing works
Cross-Model Cross-Stream Learning for Self-Supervised Human Action Recognition
Liu, M., Liu, H., & Guo, T. (2024) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2024.3467334 · Cited by 10 · see citing works
Idempotent Unsupervised Representation Learning for Skeleton-Based Action Recognition
Lin, L., Wu, L., Zhang, J., & Liu, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73347-5_5 · Cited by 10 · see citing works
MTGEA: A Multimodal Two-Stream GNN Framework for Efficient Point Cloud and Skeleton Data Alignment
Lee, G., & Kim, J. (2023) · Sensors · doi:10.3390/s23052787 · Cited by 10 · see citing works
Improving Small-Scale Human Action Recognition Performance Using a 3D Heatmap Volume
Yuan, L., He, Z., Wang, Q., Xu, L., & Ma, X. (2023) · Sensors · doi:10.3390/s23146364 · Cited by 10 · see citing works
Liu, B., Chen, W., Wang, Z., Pouriyeh, S., & Han, M. (2023) · Electronics · doi:10.3390/electronics12143084 · Cited by 10 · see citing works
Self-Supervised 3D Behavior Representation Learning Based on Homotopic Hyperbolic Embedding
Chen, J.-H., Jin, Z., Wang, Q.-C., & Meng, H. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3328230 · Cited by 12 · see citing works
Neural Network‐Oriented Big Data Model for Yoga Movement Recognition
Wang, H. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/4334024 · Cited by 10 · see citing works
Wang, K., Cao, J., Cao, B., & Liu, B. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.104076 · Cited by 9 · see citing works
Micro-gesture Recognition: A Comprehensive Survey of Datasets, Methods, and Challenges
Wang, T., Lin, X., Xu, Y. D., Ye, Q., Guo, D., Escalera, S., Khoriba, G. A., & Yu, Z. (2026) · Machine Intelligence Research · doi:10.1007/s11633-025-1629-x · Cited by 9 · see citing works
Semantic-guided multi-scale human skeleton action recognition
Qi, Y., Hu, J., Zhuang, L., & Pei, X. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03968-5 · Cited by 9 · see citing works
Li, Q., & Wang, Y. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2023.3257262 · Cited by 10 · see citing works
An Adaptive Neurofeedback Method for Attention Regulation Based on the Internet of Things
Cai, H., Zhang, Y., Xiao, H., Zhang, J., Hu, B., & Hu, X. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3083745 · Cited by 9 · see citing works
Self-citationPaoletti, G., Beyan, C., & Bue, A. D. (2022) · IEEE Access · doi:10.1109/access.2022.3229478 · Cited by 9 · see citing works
CdCLR: Clip-Driven Contrastive Learning for Skeleton-Based Action Recognition
Rong, G., Liu, X., Yang, J., & Yue, H. (2022) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip56404.2022.10008837 · Cited by 9 · see citing works
Hadikhani, P., Lai, D. T. C., & Ong, W.-H. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3266603 · Cited by 9 · see citing works
S-JEPA: A Joint Embedding Predictive Architecture for Skeletal Action Recognition
Abdelfattah, M., & Alahi, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73411-3_21 · Cited by 8 · see citing works
- Authors
- Rao, H., Xu, S., Hu, X., Cheng, J., & Hu, B.
- Affiliations
- Chinese Academy of Sciences (CN)
- Lanzhou University (CN)
- Corresponding author
- Hu, Xiping
- Cheng, Jun
- Hu, Bin
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2021-08
- Volume
- 569
- Pages
- 90-109
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W3156509901
- Cited by
- 235
- References
- 57
- Citations by year
- 2026: 21 · 2025: 35 · 2024: 66 · 2023: 63 · 2022: 36 · 2021: 14
- FWCI
- 16.09
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Information Sciences (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Lanzhou University Institutional Repository (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Human Motion and Animation
- Keywords
- 3D skeleton data, contrastive learning
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
