Skeleton-based action recognition via spatial and temporal transformer networks
Open accessThe 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
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?
TASKED extends the approach with a transformer network, accounting for sensor orientations and spatial-temporal features.
“TASKED [182] extends this with a transformer network [47,150], accounting for sensor orientations and spatial-temporal features.”
From Past, Present, and Future of Sensor-based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task (2025) · cites this paper as [150]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648 · see citing works
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.01703 · Cited by 31,473 · see citing works
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818 · see citing works
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 21,793 · see citing works
End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_13 · Cited by 18,285 · see citing works
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273 · see citing works
Wang, X., Girshick, R., Gupta, A., & He, K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00813 · Cited by 11,596 · see citing works
The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005605 · Cited by 9,582 · see citing works
Two-Stream Convolutional Networks for Action Recognition in Videos
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1406.2199 · Cited by 6,756 · see citing works
OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields
Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., & Sheikh, Y. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2929257 · Cited by 5,536 · 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 14× · see citing works
“Finally, in Figure 5b we also compare the entire stream architectures, i.e., ST-GCN (Yan et al. (2018)) and 1s-AGCN (Shi et al. (2019b)) with the proposed S-TR and T-TR streams in terms of parameters.”
BackgroundMethodInfluentialAdvances In Deep Learning On Graphs (Gsp'18 Workshop)
Defferrard, M. (2016) · Figshare · doi:10.5281/zenodo.1286817 · Cited by 4,977 · see citing works
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
(2018) · doi:10.1109/cvpr40276.2018 · Cited by 4,769 · see citing works
Spectral Networks and Locally Connected Networks on Graphs
Bruna, J., Zaremba, W., Szlam, A., & LeCun, Y. (2013) · arXiv · doi:10.48550/arxiv.1312.6203 · Cited by 3,983 · see citing works
The Kinetics Human Action Video DatasetPreprint
Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.06950 · Cited by 3,711 · see citing works
Geometric Deep Learning: Going beyond Euclidean data
Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2693418 · Cited by 3,661 · 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
Conditional Image Generation with PixelCNN DecodersPreprint
van den Oord, A., Kalchbrenner, N., Vinyals, O., Espeholt, L., Graves, A., & Kavukcuoglu, K. (2016) · Neural Information Processing Systems · doi:10.48550/arxiv.1606.05328 · Cited by 3,066 · see citing works
CCNet: Criss-Cross Attention for Semantic Segmentation
Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., & Liu, W. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00069 · Cited by 3,045 · see citing works
Microsoft Kinect Sensor and Its Effect
Zhang, Z. (2012) · IEEE Multimedia · doi:10.1109/mmul.2012.24 · Cited by 2,491 · see citing works
Learning Convolutional Neural Networks for GraphsPreprint
Niepert, M., Ahmed, M., & Kutzkov, K. (2016) · International Conference on Machine Learning · doi:10.48550/arxiv.1605.05273 · Cited by 2,311 · see citing works
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
Shi, L., Zhang, Y., Cheng, J., & Lu, H. (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.2019.01230 · Cited by 2,181 · see citing works
A new model for learning in graph domains
Gori, M., Monfardini, G., & Scarselli, F. (2006) · Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. · doi:10.1109/ijcnn.2005.1555942 · Cited by 2,075 · see citing works
Aggarwal, J. K., & Ryoo, M. S. (2011) · ACM Computing Surveys · doi:10.1145/1922649.1922653 · Cited by 2,053 · 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
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
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
Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition
Liu, J., Shahroudy, A., Xu, D., & Wang, G. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46487-9_50 · Cited by 1,353 · see citing works
Attention Augmented Convolutional Networks
Bello, I., Zoph, B., Le, Q. V., Vaswani, A., & Shlens, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00338 · Cited by 1,320 · see citing works
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition
Liu, Z., Zhang, H., Chen, Z., Wang, Z., & Ouyang, W. (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/cvpr42600.2020.00022 · Cited by 1,307 · see citing works
Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognition
Li, M., Chen, S., Chen, X., Zhang, Y., Wang, Y., & Tian, Q. C. (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.2019.00371 · Cited by 1,285 · see citing works
Deep Convolutional Networks on Graph-Structured Data
Henaff, M., Bruna, J., & LeCun, Y. (2015) · arXiv · doi:10.48550/arxiv.1506.05163 · Cited by 1,255 · see citing works
Parmar, N., Vaswani, A., Uszkoreit, J., Kaiser, Ł., Shazeer, N., Ku, A., & Tran, D. (2018) · arXiv · doi:10.48550/arxiv.1802.05751 · Cited by 1,085 · see citing works
Skeleton-Based Action Recognition With Shift Graph Convolutional Network
Cheng, K., Zhang, Y., He, X., Chen, W., Cheng, J., & Lu, H. (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/cvpr42600.2020.00026 · Cited by 1,046 · see citing works
An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data
Song, S., Lan, C., Xing, J., Zeng, W., & Liu, J. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11212 · Cited by 1,011 · see citing works
Skeleton-Based Action Recognition With Directed Graph Neural Networks
Shi, L., Zhang, Y., Cheng, J., & Lu, H. (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.2019.00810 · Cited by 979 · 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
Enhanced skeleton visualization for view invariant human action recognition
Liu, M., Liu, H., & Chen, C. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.02.030 · Cited by 871 · see citing works
Multi-context Attention for Human Pose Estimation
Chu, X., Yang, W. P., Ouyang, W., Ma, C., Yuille, A., & Wang, X. (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.2017.601 · Cited by 752 · see citing works
Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., & Zheng, N. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.233 · Cited by 691 · see citing works
Global Context-Aware Attention LSTM Networks for 3D Action Recognition
Liu, J., Wang, G., Hu, P., Duan, L., & Kot, A. C. (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.2017.391 · Cited by 677 · see citing works
Neural Network for Graphs: A Contextual Constructive Approach
Micheli, A. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2010350 · Cited by 639 · see citing works
P-CNN: Pose-Based CNN Features for Action Recognition
Cheron, G., Laptev, I., & Schmid, C. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.368 · Cited by 633 · see citing works
Protein-to-protein interactions
Cannataro, M., Guzzi, P. H., & Veltri, P. (2010) · ACM Computing Surveys · doi:10.1145/1824795.1824796 · Cited by 620 · see citing works
Skeleton-Based Human Action Recognition With Global Context-Aware Attention LSTM Networks
Liu, J., Wang, G., Duan, L., Abdiyeva, K., & Kot, A. C. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2785279 · Cited by 605 · see citing works
Skeleton-Based Human Action Recognition with Global Context-Aware Attention LSTM Networks
Liu, J., Wang, G., Duan, L.-Y., Abdiyeva, K., & Kot, A. C. (2018) · Lancaster EPrints (Lancaster University) · Cited by 531 · see citing works
Human action recognition using a temporal hierarchy of covariance descriptors on 3D joint locations
Hussein, M. E., Torki, M., Gowayyed, M. A., & El-Saban, M. (2013) · Cited by 507 · see citing works
A Comprehensive Survey of Vision-Based Human Action Recognition Methods
Zhang, H.-B., Zhang, Y.-X., Zhong, B., Lei, Q., Yang, L., Du, J.-X., & Chen, D.-S. (2019) · Sensors · doi:10.3390/s19051005 · Cited by 503 · cited 1× · see citing works
“Nevertheless, several aspects of skeleton-based action recognition still remain open (Zhang et al. (2019); Aggarwal and Ryoo (2011); Ren et al. (2020)).”
BackgroundAn End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton DataPreprint
Song, S., Lan, C., Xing, J., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.06067 · Cited by 481 · 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
Intel(R) RealSense(TM) Stereoscopic Depth Cameras
Keselman, L., Woodfill, J., Grunnet-Jepsen, A., & Bhowmik, A. K. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.167 · Cited by 455 · see citing works
Jointly Learning Heterogeneous Features for RGB-D Activity Recognition
Hu, J., Zheng, W., Lai, J., & Zhang, J. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2640292 · Cited by 392 · see citing works
Jointly learning heterogeneous features for RGB-D activity recognition
Hu, J., Zheng, W., Lai, J., & Zhang, J. (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.2015.7299172 · Cited by 358 · see citing works
Recognizing Human Actions as the Evolution of Pose Estimation Maps
Liu, M., & Yuan, J. (2018) · 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.2018.00127 · Cited by 352 · see citing works
Robust hand gesture recognition with kinect sensor
Ren, Z., Meng, J., Yuan, J., et al. (2011) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2072298.2072443 · Cited by 284 · see citing works
Learning Clip Representations for Skeleton-Based 3D Action Recognition
Ke, Q., Bennamoun, M., An, S., Sohel, F. A., & Boussaïd, F. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2812099 · Cited by 268 · 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
Li, B., Dai, Y., Cheng, X., Chen, H., Lin, Y., & He, M. (2017) · IEEE International Conference on Multimedia & Expo Workshops (ICMEW) · doi:10.1109/icmew.2017.8026282 · Cited by 252 · see citing works
RNN Fisher Vectors for Action Recognition and Image Annotation
Lev, G., Sadeh, G., Klein, B., & Wolf, L. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46466-4_50 · Cited by 170 · see citing works
Giancola, S., Valenti, M., & Sala, R. (2018) · SpringerBriefs in computer science · doi:10.1007/978-3-319-91761-0 · Cited by 155 · see citing works
Robust Spatial Filtering With Graph Convolutional Neural Networks
Such, F. P., Sah, S., Domínguez, M., Pillai, S., Zhang, C., Michael, A., Cahill, N. D., & Ptucha, R. (2017) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2017.2726981 · Cited by 145 · see citing works
Image Captioning Through Image Transformer
He, S., Liao, W., Tavakoli, H. R., Yang, M. Y., Rosenhahn, B., & Pugeault, N. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69538-5_10 · Cited by 140 · see citing works
Transformers without Tears: Improving the Normalization of Self-AttentionPreprint
Nguyen, T. Q., & Salazar, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.05895 · Cited by 124 · see citing works
Self-Attention Network for Skeleton-based Human Action Recognition
Cho, S., Maqbool, M. H., Liu, F., & Foroosh, H. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093639 · Cited by 116 · see citing works
(2018) · arXiv (Cornell University) · Cited by 113 · see citing works
A Survey on 3D Skeleton-Based Action Recognition Using Learning MethodPreprint
Ren, B., Liu, M., Ding, R., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.05907 · Cited by 85 · see citing works
Investigation of different skeleton features for CNN-based 3D action recognition
Ding, Z., Wang, P., Ogunbona, P., & Li, W. (2017) · IEEE International Conference on Multimedia & Expo Workshops (ICMEW) · doi:10.1109/icmew.2017.8026286 · Cited by 82 · see citing works
Point TransformerPreprint
Zhao, H., Jiang, L., Jia, J., Torr, P. H. S., & Koltun, V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.09164 · Cited by 75 · see citing works
Li, B., He, M., Cheng, X., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.05645 · Cited by 51 · see citing works
Parameter Efficient Multimodal Transformers for Video Representation Learning
Lee, S., Yu, Y., Kim, G., Breuel, T., Kautz, J., & Song, Y. (2020) · arXiv · doi:10.48550/arxiv.2012.04124 · Cited by 49 · see citing works
DropAttention: A Regularization Method for Fully-Connected Self-Attention NetworksPreprint
Lin, Z., Liu, P., Huang, L., Chen, J., Qiu, X., & Huang, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.11065 · Cited by 30 · see citing works
Chen, T., Wang, Z., Li, L., Li, D., Li, Z., Chang, X., Bian, P., Liang, G., Wang, Q., & Xie, T. (2025) · Schloss Dagstuhl – Leibniz-Zentrum für Informatik · doi:10.4230/lipics.ecoop.2025.6
Flagged first, then by citations
Research works printed but not linked: 5
- Bello, I., Zoph, B., Vaswani, A., Shlens, J., Le, Q.V., 2019a. Attention aug- mented convolutional networks, in: Proceedings of the IEEE/CVF Interna- tional Conference on Computer Vision (ICCV).
- Bello, I., Zoph, B., Vaswani, A., Shlens, J., Le, Q.V., 2019b. Attention aug- mented convolutional networks. Proceedings of the IEEE International Con- ference on Computer Vision , 3286-3295.
- Defferrard, M., Bresson, X., Vandergheynst, P., 2016. Convolutional neural networks on graphs with fast localized spectral filtering. Advances in neural information processing systems , 3844-3852.
- Keselman, L., Iselin Woodfill, J., Grunnet-Jepsen, A., Bhowmik, A., 2017. Intel realsense stereoscopic depth cameras, in: Proceedings of the IEEE Confer- ence on Computer Vision and Pattern Recognition Workshops, pp. 1-10.
- Kipf, T.N., Welling, M., 2017. Semi-supervised classification with graph con- volutional networks. 5th International Conference on Learning Representa- tions, ICLR .
Cited by
Sorted by citations
Haresamudram, H., Tang, C. I., Suh, S., Lukowicz, P., & Plötz, T. (2025) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3729467 · Cited by 33 · see citing works
“TASKED [174] extended this with a transformer network [45, 142], accounting for sensor orientations and spatial-temporal features.”
Haresamudram, H., Tang, C. I., Suh, S., Lukowicz, P., & Ploetz, T. (2024) · arXiv · Cited by 31 · see citing works
“TASKED [174] extended this with a transformer network [45, 142], accounting for sensor orientations and spatial-temporal features.”
- Authors
- Plizzari, C., Cannici, M., & Matteucci, M.
- Affiliations
- Politecnico di Torino (IT)
- Politecnico di Milano (IT)
- Corresponding author
- Plizzari, Chiara
- Venue
- Computer Vision and Image Understanding
- Publisher
- Elsevier BV
- Published
- 2021-07
- Volume
- 208-209
- Pages
- 103219
- ISSN
- 1077-3142 (ISSN-L)
- OpenAlex
- W3113067059
- Cited by
- 409
- References
- 73
- Citations by year
- 2026: 58 · 2025: 80 · 2024: 95 · 2023: 104 · 2022: 54 · 2021: 18
- FWCI
- 24.37
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Computer Vision and Image Understanding (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- PORTO@Iris (submittedVersion, cc-by-nc-nd)
- Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion, other-oa)
- Archivio istituzionale della Ricerca - Bocconi (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- 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 Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- skeleton-based action recognition, human activity recognition, spatio-temporal transformer, self-attention, spatial self-attention, temporal self-attention, two-stream network, skeleton graph, 3D skeleton data, inter-frame correlation
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