The DOI matches its Crossref record; 44 of the 57 references it cites were found in a registry or an index.
Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition
Open accessOverview
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
Cites
Statistics
Verification
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.
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?
In the citing paper
“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
In the citing paper
“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
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
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
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,119
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 20,507
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
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
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
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×
“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
Research works printed but not linked: 8
- Xu, 2020, Attention based multi-level co-occurrence graph convolutional lstm for 3d action recognition, IEEE Internet of Things Journal, 1
- Y. Tian, D. Krishnan, P. Isola, Contrastive multiview coding, arXiv preprint arXiv:1906.05849. · arXiv
- A. v. d. Oord, Y. Li, O. Vinyals, Representation learning with contrastive predictive coding, arXiv preprint arXiv:1807.03748. · arXiv
- X. Chen, H. Fan, R. Girshick, K. He, Improved baselines with momentum contrastive learning, arXiv preprint arXiv:2003.04297. · arXiv
- Unsupervised learning of visual features by contrasting cluster assignments, in: Caron, Mathilde and Misra, Ishan and Mairal, Julien and Goyal, Priya and Bojanowski, Piotr and Joulin, Armand, vol. 33, 2020.
- X. Chen, K. He, Exploring simple siamese representation learning, arXiv preprint arXiv:2011.10566. · arXiv
- P. Goyal, P. Dollár, R.B. Girshick, P. Noordhuis, L. Wesolowski, A. Kyrola, A. Tulloch, Y. Jia, K. He, Accurate, large minibatch sgd: Training imagenet in 1 hour, arXiv preprint arXiv:1706.02677. · arXiv
- J. Weston, S. Chopra, A. Bordes, Memory networks, in: International Conference on Learning Representations, 2015.
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
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
“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 228
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
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 202
“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…”
BackgroundMethodInfluential
Metadata
- 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 › Computer Science › Computer Graphics and Computer-Aided Design › 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
