Pith. sign in

REVIEW 4 major objections 6 minor 45 references

Topological Symmetry Enhanced Graph Convolution for Skeleton-Based Action Recognition

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Exploiting left–right body symmetry in graph topology, together with frame-wise deformable temporal convolution, lets skeleton action recognition match state-of-the-art at a fraction of the parameters.

desk verdict Competent incremental GCN paper with a defensible efficiency story, but the symmetry mechanism that gives it its name is asserted rather than enforced. read the letter →

arxiv 2411.12560 v2 pith:SYFKXONA submitted 2024-11-19 cs.CV cs.AI

Zeyu Liang , Hailun Xia , Naichuan Zheng , Huan Xu This is my paper · ORCID
classification cs.CVcs.AI
keywords skeleton-basedactionrecognitiongraphconvolutionalnetworkstopologicalsymmetrydeformabletemporalconvolutionhumanbodyefficientNTURGB+D120k-nearest-neighbortopology
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper is trying to establish that a skeleton-based action recognizer can match or nearly match the best published accuracy while using far fewer parameters, if the graph topology is made aware of the human body's left–right symmetry and the temporal filter is allowed to learn frame-specific sampling offsets. The central proposal is TSE-GCN, built from TSE-GC (topological symmetry enhanced graph convolution) and MBDTC (multi-branch deformable temporal convolution). On NTU RGB+D 120, the reported single-stream model reaches 86.6% cross-subject and 88.2% cross-set with 1.1M parameters and 1.38 GFLOPs, and the four-stream fusion reaches 90.0% and 91.1%. The authors further report that the symmetry-aware reactivation alone adds accuracy over both a purely physical and a purely flexible topology in their ablations. If the claim holds, physical priors about body symmetry are a cheap and effective substitute for larger learned topology models.

What carries the argument

The load-bearing mechanism is the reactivation identity $A_s = H_s \odot M$ (Eq. 9): elementwise multiplication of a shared learned adjacency $M$ with a per-sample, per-partition scale mask $H$. $H$ is generated by $k$-nearest-neighbor search on joint embeddings, with the selected neighbors mapped through a shortest-path-distance table along the physical skeleton, so the mask selects which hop-level correlations are active and which stay fixed. This single operation turns one shared topology into $K$ channel-partition-specific topologies and is what the paper claims imposes the left–right symmetry prior. The second mechanism is MBDTC's deformable temporal convolution, which computes a graph readout, predicts an offset per frame from it, and samples/reweights the temporal filter with those offsets, giving each frame its own receptive field.

What would settle it

Train the same TSE-GCN but replace the scale mask $H$ in Eq. 9 with a mask of equal sparsity whose k-nearest-neighbor indices are computed from a mirrored copy of the joint embedding (left wrist swapped with right wrist, etc.); if accuracy does not drop, left-right symmetry is not what the reactivation supplies. A complementary check is to measure the mirror asymmetry of the learned $A_s$ at convergence, comparing entries for homologous joint pairs; a near-symmetric $A_s$ would support the claim, a far-from-symmetric one would refute the symmetry story.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that a constrained, symmetry-aware topology can beat both extremes of the topology design space. TSE-GC learns a shared adjacency matrix $M$ and, for each sample, a scale mask $H$: each joint picks its $k$ nearest neighbors in the embedding space, those indices are mapped to hop levels via shortest-path distances along the skeleton, and the elementwise product $A_s = H_s \odot M$ reactivates only those correlations. This reactivated topology is then combined with a data-dependent calibration term $B$ and a fully learnable per-partition adjacency $C_s$. The paper's ablation shows the combined constrained-plus-flexible topology (86.6%) outperforms the flexible-only branch (86.4%) and the constrained-only branch (86.2%) on NTU RGB+D 120 cross-subject, and that on 27 symmetry-related classes TSE-GC gains +0.6% over a physical topology and +0.8% over a nearly flexible topology. Independently, MBDTC replaces fixed multi-scale temporal convolution with deformable 1D convolution that learns a separate offset for each frame, improving accuracy by 0.5% while cutting 1.9M parameters.

Load-bearing premise

The symmetry benefit assumes that the k-nearest-neighbor scale mask and the shared topology actually mirror the body's left–right structure, but nothing in the equations forces mirror symmetry, so the learned reactivation could turn out asymmetric and TSE-GC would then just be a flexible topology learner with extra knobs.

Editorial extensions

If this is right

  • On the reported numbers, a single-stream TSE-GCN with 1.1M parameters matches BlockGCN's 88.2% cross-set accuracy on NTU RGB+D 120 while using fewer FLOPs (1.38 vs 2.05G).
  • The ablation implies the symmetry reactivation is doing measurable work: removing it (Cs-only) drops accuracy by 0.2%, and removing the flexible branch (As+B only) drops it by 0.4% relative to the full TSE-GC.
  • MBDTC's 0.5% gain with a 1.9M parameter reduction suggests existing fixed multi-scale temporal convolutions waste capacity; deformable temporal sampling could replace them in other skeleton GCNs.
  • The model can act as a backbone for further improvements, since the paper reports its accuracy can be pushed higher by combining with augmentation or classification-head methods.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the symmetry mechanism is the real driver, then explicitly tying the weights of mirrored left–right joint pairs (a hard mirror-symmetry constraint) should further reduce parameters without hurting accuracy; the paper does not test this.
  • The k-NN/SPD mechanism is skeleton-agnostic, so the same reactivation recipe could transfer to other symmetric articulated structures—robotic arms, animal skeletons, hand skeletons—where mirror correspondences are known in advance.
  • Because Eq. 9 never enforces mirror symmetry, the learned $A_s$ could converge to an asymmetric matrix; checking the mirror asymmetry of $A_s$ would tell whether 'symmetry awareness' is a genuine prior or an incidental regularizer.
  • The per-frame offset mechanism of MBDTC is not specific to skeletons; it could be lifted into generic 1D convolutional networks for any time series where sampling positions should adapt to the signal.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper proposes TSE-GCN for skeleton-based action recognition, combining a Topological Symmetry Enhanced Graph Convolution (TSE-GC) with a Multi-Branch Deformable Temporal Convolution (MBDTC). TSE-GC learns a shared topology that is reactivated per channel-partition via k-NN masks derived from learned embeddings and shortest-path distances, with additional flexible calibration and learnable adjacency terms. MBDTC adapts deformable convolution to the temporal dimension, learning per-frame offsets via graph readout. The authors report competitive accuracy on NTU RGB+D, NTU RGB+D 120, and NW-UCLA, with lower parameter counts and FLOPs than several recent methods, and ablate the components on NTU 120 cross-subject.

Significance. If the symmetry-aware mechanism is genuine, the paper offers a useful efficiency-oriented contribution to skeleton-based action recognition: it shows competitive accuracy with substantially fewer parameters and FLOPs than many state-of-the-art graph convolution models, while also introducing a deformable temporal module. The benchmark protocol is standard, the ablations are internally consistent, and the comparisons are thoughtfully handled (e.g., recalculated FLOPs, separated non-directly-comparable methods). The central mechanistic claim, however, that the gains come from an explicit topological symmetry prior, is not supported by the equations as written, because no mirror-symmetry constraint is imposed. The absence of error bars and the use of the same split for hyperparameter selection and final reporting further weaken the quantitative component claims. The paper is worth a substantive revision to either enforce symmetry or reframe the contribution as a flexible topology learner with an SPD-derived mask prior.

major comments (4)
  1. [Section 3.2, Eqs. (7)–(10)] The load-bearing claim that TSE-GC 'incorporates topological symmetry awareness' is not guaranteed by the formulation. The scale mask H is generated by KNN(θ(X), K) on learned embeddings, and nothing in Eq. (7) requires H to be invariant under a mirror map of the body. Even if H were mirror-symmetric, Eq. (9) multiplies it by an unconstrained learned dense matrix M, and Eq. (10) adds unconstrained terms B and Cs, so the final topology Z need not be left-right symmetric. Thus the improvement attributed to symmetry could instead come from a flexible topology learner with an SPD-derived mask prior. Please add a hard-symmetrization ablation (e.g., explicitly symmetrize As or H under a joint mirror map) and/or directly measure the mirror-symmetry violation of the learned As; otherwise the central novelty label is unsupported.
  2. [Section 4.4, Table 2] The component-level accuracy claims are based on single numbers with no reported variance. Several differences are very small: 85.5 vs 85.4 for PE, 86.2 vs 86.4 between Cs and As+B, 86.6 vs 86.5 for the final configuration. On standard skeleton benchmarks, run-to-run variation can exceed these margins, so the 0.7% and 1.7% improvements in the row 'TSE-GC' are not clearly distinguishable from noise. Please report mean and standard deviation over at least three seeds, or explicitly state if only a single run was performed and discuss the implications.
  3. [Section 4.4, Table 3 and Section 4.3, Table 1] The hyperparameters K and R are tuned on the NTU RGB+D 120 cross-subject split, and the same split is later reported as the headline X-sub result in Table 1. This makes the 90.0% X-sub number a post-selection result, not an independent evaluation. Moreover, the chosen configuration K=3, R=8 is not the best-accuracy setting in Table 3 (86.6 vs 86.7 for K=3, R=4); the choice is justified by efficiency, but this should be acknowledged as a model-selection decision on the test split. Please either report results with hyperparameters fixed before test-split evaluation or add a clear statement of this limitation.
  4. [Section 4.5, Fig. 4] The GPT-4-based class analysis is post-hoc and does not test the symmetry mechanism. The class list is selected after seeing the results (27 'symmetry related classes'), and the analysis compares per-class accuracy without inspecting whether the learned As matrices are actually mirror-symmetric. The average improvements of +0.6% and +0.8% on these classes are suggestive but not mechanistic evidence. A direct test would be to compare the learned topology with its mirror image (e.g., correlation between As and a symmetrized version) or to pre-register the class list independently of the experimental outcomes. Without this, the analysis in Section 4.5 does not resolve the concern raised about Eqs. (7)–(10).
minor comments (6)
  1. [Throughout] The module name MBDTC is inconsistently written as 'MBTDC' in several places, including the caption of Table 2, the text of Section 4.4, and the conclusion; please unify.
  2. [Section 4.2] There are several typos: 'weight deacy' should be 'weight decay', 'Nestrov' should be 'Nesterov', and 'adopt he data-processing' should be 'adopt the data-processing'.
  3. [Section 3.3, Eq. (16)] Equation (16) uses X(l+1) on both the left-hand side and inside the sampling function Γ(·,·,X(l+1),·), which is confusing because the equation appears to define X(l+1) in terms of itself. Please clarify the notation for the input and output feature maps.
  4. [Section 3.4] The sentence 'L = 9 , is the number of times our basic block is stacked' is grammatically incomplete; please rewrite.
  5. [Figure 1] The caption of Figure 1 contains the stray text 'reactivate0 2' which appears to be corrupted; please fix it.
  6. [References] Reference [16] (Kim and Mnih, 'Disentangling by factorising') appears unrelated to skeleton-based action recognition; please verify that this citation is intentional and needed.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation found: the accuracy and ablation claims are empirical, externally benchmarked, and do not reduce to the model's definitions.

full rationale

This paper makes no formal claim whose output is equivalent to its input by construction. The central results—accuracy on NTU RGB+D, NTU RGB+D 120, and NW-UCLA and the component ablations in Tables 2–4—are measured on held-out benchmarks against external baselines, so they are falsifiable rather than forced by the definitions in Eqs. (6)–(12). The topology formulas introduce learned matrices M, B, and Cs and an SPD-based scale mask H; none of these definitions logically entails the reported improvements. The paper also does not rely on load-bearing self-citation: deformable convolution [11], the Info-GCN positional embedding [10], and the CTR-GCN/BlockGCN comparisons are independent prior work. The legitimate concerns—that the 'topological symmetry awareness' label is not enforced as a hard mirror-symmetry constraint in Eq. (9), and that the GPT-4 class analysis in Section 4.5 is post-hoc—are correctness and interpretability issues about whether the mechanism label is supported, not circular reductions of the paper's own reasoning. Under the stated rules for flagging circularity, no step qualifies.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The paper makes an empirical architecture claim, so the ledger records hand-chosen hyperparameters and domain assumptions instead of conservation principles. The free parameters are standard for this literature, but they are tuned on the same benchmark used for final reporting. No new physical entities are introduced.

free parameters (5)
  • K (number of channel partitions and k-NN neighbors) = 3
    Selected by ablation on NTU120 X-sub (Table 3); K=7 makes As fully connected, so K is a hand-chosen capacity knob.
  • R (channel reduction ratio for TSE-GC) = 8
    Selected by ablation on NTU120 X-sub; R=4 gives slightly better accuracy (86.7 vs 86.6) but R=8 is chosen for efficiency.
  • DTC dilation rates in MBDTC = 1 and 2
    Two DTC branches use dilations 1 and 2 following the MS-TCN design; no ablation justifies these specific values.
  • Network depth and channel schedule = 9 blocks, 64-64-64-128-128-128-256-256
    Taken from prior GCN designs and not varied in ablations.
  • Activation functions (GeLU and Tanh) = GeLU in blocks, Tanh for Eq. 6
    Chosen based on experimental results per Section 3.4; no reported ablation is provided.
assumptions (5)
  • domain assumption Left-right topological symmetry of the human body is a useful prior for action recognition
    Central motivation in Section 1; intuitive but not independently validated against asymmetric topology learners.
  • domain assumption Shortest path distance over the physical skeleton defines meaningful interaction scales
    Used to map k-NN neighbors to scale masks in Eqs. 7-8; no evidence that SPD hops align with action-relevant joint interactions.
  • domain assumption k-NN in embedding space identifies the relevant interaction partners for each sample
    Eq. 7; the choice of k-NN over Euclidean distance is not compared with other neighborhood construction schemes.
  • standard math Standard normalized graph convolution (Eq. 1) is the correct base aggregator
    Adopted from Kipf and Welling [17] and prior skeleton GCN literature; not re-derived here.
  • domain assumption Deformable offsets computed from graph-pooled temporal features remain stable during training
    Section 3.3; no analysis of offset drift, regularization, or failure modes.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Topological Symmetry Enhanced Graph Convolution for Skeleton-Based Action Recognition." pith.science (2026). https://pith.science/paper/SYFKXONA

@misc{pith2026241112560,
  author       = {Pith},
  title        = {Pith review of: Topological Symmetry Enhanced Graph Convolution for Skeleton-Based Action Recognition},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SYFKXONA}},
  note         = {Machine review of arXiv:2411.12560}
}
read the original abstract

Skeleton-based action recognition has achieved remarkable performance with the development of graph convolutional networks (GCNs). However, most of these methods tend to construct complex topology learning mechanisms while neglecting the inherent symmetry of the human body. Additionally, the use of temporal convolutions with certain fixed receptive fields limits their capacity to effectively capture dependencies in time sequences. To address the issues, we (1) propose a novel Topological Symmetry Enhanced Graph Convolution (TSE-GC) to enable distinct topology learning across different channel partitions while incorporating topological symmetry awareness and (2) construct a Multi-Branch Deformable Temporal Convolution (MBDTC) for skeleton-based action recognition. The proposed TSE-GC emphasizes the inherent symmetry of the human body while enabling efficient learning of dynamic topologies. Meanwhile, the design of MBDTC introduces the concept of deformable modeling, leading to more flexible receptive fields and stronger modeling capacity of temporal dependencies. Combining TSE-GC with MBDTC, our final model, TSE-GCN, achieves competitive performance with fewer parameters compared with state-of-the-art methods on three large datasets, NTU RGB+D, NTU RGB+D 120, and NW-UCLA. On the cross-subject and cross-set evaluations of NTU RGB+D 120, the accuracies of our model reach 90.0\% and 91.1\%, with 1.1M parameters and 1.38 GFLOPS for one stream.

Figures

Figures reproduced from arXiv: 2411.12560 by the authors.

Figure 1
Figure 1. Topology reactivation with symmetry awareness. The [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Architecture overview of the proposed TSE-GCN. PE denotes the learnable absolute positional embedding[ [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Sampling mechanism in our DTC Module. The receptive [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Accuracy difference(%) between TSE-GC and two rep [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

45 extracted references · 33 canonical work pages

  1. [1]

    Gpt-4 technical report

    Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ah- mad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 ,

    arXiv

  2. [2]

    On-line simultaneous learning and recognition of everyday activities from virtual reality per- formances

    Tamas Bates, Karinne Ramirez-Amaro, Tetsunari Inamura, and Gordon Cheng. On-line simultaneous learning and recognition of everyday activities from virtual reality per- formances. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pages 3510–3515. IEEE, 2017. 1

    work page 2017

  3. [3]

    Realtime multi-person 2d pose estimation using part affinity fields

    Zhe Cao, Tomas Simon, Shih-En Wei, and Yaser Sheikh. Realtime multi-person 2d pose estimation using part affinity fields. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7291–7299, 2017. 1

    work page 2017

  4. [4]

    End-to- end object detection with transformers

    Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to- end object detection with transformers. In European confer- ence on computer vision, pages 213–229. Springer, 2020. 2

    2020

  5. [5]

    Channel-wise topology refinement graph convolution for skeleton-based action recognition

    Yuxin Chen, Ziqi Zhang, Chunfeng Yuan, Bing Li, Ying Deng, and Weiming Hu. Channel-wise topology refinement graph convolution for skeleton-based action recognition. In Proceedings of the IEEE/CVF international conference on computer vision, pages 13359–13368, 2021. 1, 2, 5, 6

    work page 2021

  6. [6]

    Multi-scale spatial temporal graph convolutional net- work for skeleton-based action recognition

    Zhan Chen, Sicheng Li, Bing Yang, Qinghan Li, and Hong Liu. Multi-scale spatial temporal graph convolutional net- work for skeleton-based action recognition. In Proceed- ings of the AAAI conference on artificial intelligence , pages 1113–1122, 2021. 6

    work page 2021

  7. [7]

    Higherhrnet: Scale- aware representation learning for bottom-up human pose es- timation

    Bowen Cheng, Bin Xiao, Jingdong Wang, Honghui Shi, Thomas S Huang, and Lei Zhang. Higherhrnet: Scale- aware representation learning for bottom-up human pose es- timation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5386–5395,

  8. [8]

    Decoupling gcn with dropgraph module for skeleton-based action recognition

    Ke Cheng, Yifan Zhang, Congqi Cao, Lei Shi, Jian Cheng, and Hanqing Lu. Decoupling gcn with dropgraph module for skeleton-based action recognition. In Computer Vision– ECCV 2020: 16th European Conference, Glasgow, UK, Au- gust 23–28, 2020, Proceedings, Part XXIV 16 , pages 536–

    work page 2020

Show all 45 references
  1. [9]

    Skeleton-based action recognition with shift graph convolutional network

    Ke Cheng, Yifan Zhang, Xiangyu He, Weihan Chen, Jian Cheng, and Hanqing Lu. Skeleton-based action recognition with shift graph convolutional network. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 183–192, 2020. 6, 7

    2020

  2. [10]

    In- fogcn: Representation learning for human skeleton-based action recognition

    Hyung-gun Chi, Myoung Hoon Ha, Seunggeun Chi, Sang Wan Lee, Qixing Huang, and Karthik Ramani. In- fogcn: Representation learning for human skeleton-based action recognition. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition , pages 20186–20...

    2022

  3. [11]

    Deformable convolutional networks

    Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei. Deformable convolutional networks. In Proceedings of the IEEE international confer- ence on computer vision, pages 764–773, 2017. 2, 5

    2017

  4. [12]

    Hierarchical recur- rent neural network for skeleton based action recognition

    Yong Du, Wei Wang, and Liang Wang. Hierarchical recur- rent neural network for skeleton based action recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1110–1118, 2015. 2

    2015

  5. [13]

    Efficient and robust skeleton- based quality assessment and abnormality detection in hu- man action performance

    Amr Elkholy, Mohamed E Hussein, Walid Gomaa, Dima Damen, and Emmanuel Saba. Efficient and robust skeleton- based quality assessment and abnormality detection in hu- man action performance. IEEE journal of biomedical and health informatics, 24(1):280–291, 2019. 1

    2019

  6. [14]

    Graph contrastive learn- ing for skeleton-based action recognition

    Xiaohu Huang, Hao Zhou, Jian Wang, Haocheng Feng, Junyu Han, Errui Ding, Jingdong Wang, Xinggang Wang, Wenyu Liu, and Bin Feng. Graph contrastive learn- ing for skeleton-based action recognition. arXiv preprint arXiv:2301.10900, 2023. 7

    2023 arXiv

  7. [15]

    Human action recognition in uncon- strained videos by explicit motion modeling

    Yu-Gang Jiang, Qi Dai, Wei Liu, Xiangyang Xue, and Chong-Wah Ngo. Human action recognition in uncon- strained videos by explicit motion modeling. IEEE Trans- actions on Image Processing, 24(11):3781–3795, 2015. 1

    2015

  8. [16]

    Disentangling by factoris- ing

    Hyunjik Kim and Andriy Mnih. Disentangling by factoris- ing. In International conference on machine learning, pages 2649–2658. PMLR, 2018. 1

    2018

  9. [17]

    Semi-supervised classi- fication with graph convolutional networks

    Thomas N Kipf and Max Welling. Semi-supervised classi- fication with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016. 3

    2016 arXiv

  10. [18]

    Hierarchically decomposed graph convolutional net- works for skeleton-based action recognition

    Jungho Lee, Minhyeok Lee, Dogyoon Lee, and Sangyoun Lee. Hierarchically decomposed graph convolutional net- works for skeleton-based action recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vi- sion, pages 10444–10453, 2023. 1, 2, 6, 7

    2023

  11. [19]

    Skeleton-based action recognition with convolutional neural networks

    Chao Li, Qiaoyong Zhong, Di Xie, and Shiliang Pu. Skeleton-based action recognition with convolutional neural networks. In 2017 IEEE international conference on multi- media & expo workshops (ICMEW) , pages 597–600. IEEE,

    2017

  12. [20]

    Actional-structural graph convolutional networks for skeleton-based action recognition

    Maosen Li, Siheng Chen, Xu Chen, Ya Zhang, Yanfeng Wang, and Qi Tian. Actional-structural graph convolutional networks for skeleton-based action recognition. In Proceed- ings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3595–3603, 2019. 1, 2, 3

    2019

  13. [21]

    Ntu rgb+ d 120: A large- scale benchmark for 3d human activity understanding

    Jun Liu, Amir Shahroudy, Mauricio Perez, Gang Wang, Ling-Yu Duan, and Alex C Kot. Ntu rgb+ d 120: A large- scale benchmark for 3d human activity understanding. IEEE transactions on pattern analysis and machine intelligence , 42(10):2684–2701, 2019. 2, 6 9

    2019

  14. [22]

    Disentangling and unifying graph convo- lutions for skeleton-based action recognition

    Ziyu Liu, Hongwen Zhang, Zhenghao Chen, Zhiyong Wang, and Wanli Ouyang. Disentangling and unifying graph convo- lutions for skeleton-based action recognition. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 143–152, 2020. 1, 2, 5, 6

    2020

  15. [23]

    Degcn: Deformable graph convolutional networks for skeleton-based action recognition

    Woomin Myung, Nan Su, Jing-Hao Xue, and Guijin Wang. Degcn: Deformable graph convolutional networks for skeleton-based action recognition. IEEE Transactions on Image Processing, 33:2477–2490, 2024. 2

    2024

  16. [24]

    Skeleton-based action recognition via spatial and temporal transformer networks

    Chiara Plizzari, Marco Cannici, and Matteo Matteucci. Skeleton-based action recognition via spatial and temporal transformer networks. Computer Vision and Image Under- standing, 208:103219, 2021. 1

    2021

  17. [25]

    A survey on vision-based human action recognition

    Ronald Poppe. A survey on vision-based human action recognition. Image and vision computing , 28(6):976–990,

  18. [26]

    Ntu rgb+ d: A large scale dataset for 3d human activity anal- ysis

    Amir Shahroudy, Jun Liu, Tian-Tsong Ng, and Gang Wang. Ntu rgb+ d: A large scale dataset for 3d human activity anal- ysis. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1010–1019, 2016. 2, 6

    2016

  19. [27]

    Skeleton-based action recognition with directed graph neu- ral networks

    Lei Shi, Yifan Zhang, Jian Cheng, and Hanqing Lu. Skeleton-based action recognition with directed graph neu- ral networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7912–7921,

  20. [28]

    Two- stream adaptive graph convolutional networks for skeleton- based action recognition

    Lei Shi, Yifan Zhang, Jian Cheng, and Hanqing Lu. Two- stream adaptive graph convolutional networks for skeleton- based action recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 12026–12035, 2019. 1, 2, 6

    2019

  21. [29]

    Skeleton-based action recognition with multi-stream adap- tive graph convolutional networks

    Lei Shi, Yifan Zhang, Jian Cheng, and Hanqing Lu. Skeleton-based action recognition with multi-stream adap- tive graph convolutional networks. IEEE Transactions on Image Processing, 29:9532–9545, 2020. 2

    2020

  22. [30]

    Constructing stronger and faster baselines for skeleton-based action recognition

    Yi-Fan Song, Zhang Zhang, Caifeng Shan, and Liang Wang. Constructing stronger and faster baselines for skeleton-based action recognition. IEEE transactions on pattern analysis and machine intelligence, 45(2):1474–1488, 2022. 6

    2022

  23. [31]

    Interpretable 3d human ac- tion analysis with temporal convolutional networks

    Tae Soo Kim and Austin Reiter. Interpretable 3d human ac- tion analysis with temporal convolutional networks. In Pro- ceedings of the IEEE conference on computer vision and pat- tern recognition workshops, pages 20–28, 2017. 2

    2017

  24. [32]

    Deep high-resolution representation learning for human pose es- timation

    Ke Sun, Bin Xiao, Dong Liu, and Jingdong Wang. Deep high-resolution representation learning for human pose es- timation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5693–5703,

  25. [33]

    Modeling temporal dynamics and spatial configurations of actions using two- stream recurrent neural networks

    Hongsong Wang and Liang Wang. Modeling temporal dynamics and spatial configurations of actions using two- stream recurrent neural networks. In Proceedings of the IEEE conference on computer vision and pattern recogni- tion, pages 499–508, 2017. 2

    2017

  26. [34]

    Cross-view action modeling, learning and recog- nition

    Jiang Wang, Xiaohan Nie, Yin Xia, Ying Wu, and Song- Chun Zhu. Cross-view action modeling, learning and recog- nition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2649–2656, 2014. 2, 7

    2014

  27. [35]

    Neural koop- man pooling: Control-inspired temporal dynamics encod- ing for skeleton-based action recognition

    Xinghan Wang, Xin Xu, and Yadong Mu. Neural koop- man pooling: Control-inspired temporal dynamics encod- ing for skeleton-based action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10597–10607, 2023. 1

    2023

  28. [36]

    Language knowledge-assisted representation learn- ing for skeleton-based action recognition

    Haojun Xu, Yan Gao, Zheng Hui, Jie Li, and Xinbo Gao. Language knowledge-assisted representation learn- ing for skeleton-based action recognition. arXiv preprint arXiv:2305.12398, 2023. 2

    2023 arXiv

  29. [37]

    Spatial tempo- ral graph convolutional networks for skeleton-based action recognition

    Sijie Yan, Yuanjun Xiong, and Dahua Lin. Spatial tempo- ral graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on arti- ficial intelligence, 2018. 1, 2, 3, 4, 6, 7

    2018

  30. [38]

    Dynamic gcn: Context-enriched topol- ogy learning for skeleton-based action recognition

    Fanfan Ye, Shiliang Pu, Qiaoyong Zhong, Chao Li, Di Xie, and Huiming Tang. Dynamic gcn: Context-enriched topol- ogy learning for skeleton-based action recognition. In Pro- ceedings of the 28th ACM international conference on mul- timedia, pages 55–63, 2020. 2

    2020

  31. [39]

    Effects of camera viewing angles on tracking kinematic gait patterns using azure kinect, kinect v2 and orbbec astra pro v2

    Ling-Fung Yeung, Zhenqun Yang, Kenneth Chik-Chi Cheng, Dan Du, and Raymond Kai-Yu Tong. Effects of camera viewing angles on tracking kinematic gait patterns using azure kinect, kinect v2 and orbbec astra pro v2. Gait & pos- ture, 87:19–26, 2021. 1

    2021

  32. [40]

    Shap-mix: Shapley value guided mixing for long-tailed skeleton based action recognition

    Jiahang Zhang, Lilang Lin, and Jiaying Liu. Shap-mix: Shapley value guided mixing for long-tailed skeleton based action recognition. arXiv preprint arXiv:2407.12312, 2024. 6, 7

    2024 arXiv

  33. [41]

    Semantics-guided neural networks for efficient skeleton-based human action recog- nition

    Pengfei Zhang, Cuiling Lan, Wenjun Zeng, Junliang Xing, Jianru Xue, and Nanning Zheng. Semantics-guided neural networks for efficient skeleton-based human action recog- nition. In proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1112–1121,

  34. [42]

    Microsoft kinect sensor and its effect

    Zhengyou Zhang. Microsoft kinect sensor and its effect. IEEE multimedia, 19(2):4–10, 2012. 1

    2012

  35. [43]

    Learn- ing discriminative representations for skeleton based action recognition

    Huanyu Zhou, Qingjie Liu, and Yunhong Wang. Learn- ing discriminative representations for skeleton based action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10608– 10617, 2023. 6, 7

    2023

  36. [44]

    Blockgcn: Redefine topology aware- ness for skeleton-based action recognition

    Yuxuan Zhou, Xudong Yan, Zhi-Qi Cheng, Yan Yan, Qi Dai, and Xian-Sheng Hua. Blockgcn: Redefine topology aware- ness for skeleton-based action recognition. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2049–2058, 2024. 1, 2, 5, 6, 7, 8

    2024

  37. [45]

    Deformable detr: Deformable trans- formers for end-to-end object detection

    Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai. Deformable detr: Deformable trans- formers for end-to-end object detection. arXiv preprint arXiv:2010.04159, 2020. 2 10

    2010 arXiv

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.