{"id":47566216,"url":"https://github.com/firework8/Awesome-Skeleton-based-Action-Recognition","last_synced_at":"2026-04-13T23:00:58.704Z","repository":{"id":161307135,"uuid":"602405635","full_name":"firework8/Awesome-Skeleton-based-Action-Recognition","owner":"firework8","description":"A curated paper list of awesome skeleton-based action 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\n\n[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)\n[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)\n\nWe collect existing papers on skeleton-based action recognition published in prominent conferences and journals. \n\nThis paper list will be continuously updated at the end of each month. \n\n\n## Table of Contents\n\n- [Survey](#survey)\n- [Papers](#papers)\n  - [2026](#2026)\n  - [2025](#2025)\n  - [2024](#2024)\n  - [2023](#2023)\n  - [2022](#2022)\n  - [2021](#2021)\n  - [2020](#2020)\n  - [2019](#2019)\n  - [2018](#2018)\n  - [2017](#2017)\n  - [2016](#2016)\n  - [2015](#2015)\n  - [2014](#2014)\n- [Other Resources](#other-resources)\n\n## Survey\n\n- Human Action Recognition from Various Data Modalities: A Review (**TPAMI 2022**) [[paper](https://ieeexplore.ieee.org/abstract/document/9795869)]\n- Human action recognition and prediction: A survey (**IJCV 2022**) [[paper](https://link.springer.com/article/10.1007/s11263-022-01594-9)]\n- Self-Supervised Skeleton-Based Action Representation Learning: A Benchmark and Beyond (**IJCV 2026**) [[paper](https://link.springer.com/article/10.1007/s11263-025-02644-8)]\n- A Systematic Review of Skeleton-Based Action Recognition: Methods, Challenges, and Future Directions (**TNNLS 2025**) [[paper](https://ieeexplore.ieee.org/document/11282488)]\n- Transformer for Skeleton-based action recognition: A review of recent advances (**Neurocomputing 2023**) [[paper](https://www.sciencedirect.com/science/article/pii/S0925231223002217)]\n- Action recognition based on RGB and skeleton data sets: A survey (**Neurocomputing 2022**) [[paper](https://www.sciencedirect.com/science/article/pii/S0925231222011596)]\n- A Comparative Review of Recent Kinect-based Action Recognition Algorithms (**TIP 2019**) [[paper](https://ieeexplore.ieee.org/abstract/document/8753686)]\n- Representation-Centric Survey of Skeletal Action Recognition and the ANUBIS Benchmark (**2025 arXiv paper**) [[paper](https://www.researchgate.net/publication/394518696_Representation-Centric_Survey_of_Skeletal_Action_Recognition_and_the_ANUBIS_Benchmark)]\n- 3D Skeleton-Based Action Recognition: A Review (**2025 arXiv paper**) [[paper](https://arxiv.org/abs/2506.00915)]\n- The Journey of Action Recognition (**2025 arXiv paper**) [[paper](https://www.researchgate.net/profile/Lei-Wang-358/publication/387707420_The_Journey_of_Action_Recognition/links/677880dee74ca64e1f4b7bc9/The-Journey-of-Action-Recognition.pdf)]\n- A Comprehensive Methodological Survey of Human Activity Recognition Across Divers Data Modalities (**2024 arXiv paper**) [[paper](https://arxiv.org/abs/2409.09678)]\n\n## Papers\n\nStatistics: 🔥 relatively highly cited | ⭐ code is available and star \u003e 100\n\n### 2026\n\n**CVPR**\n- SkeletonContext: Skeleton-side Context Prompt Learning for Zero-Shot Skeleton-based Action Recognition [[paper](https://arxiv.org/abs/2603.29692)] [[code](https://github.com/NingWang2049/skeletoncontext)]\n- LaDy: Lagrangian-Dynamic Informed Network for Skeleton-based Action Segmentation via Spatial-Temporal Modulation [[paper](https://arxiv.org/abs/2603.24097)] [[code](https://github.com/HaoyuJi/LaDy)]\n- Spectral Scalpel: Amplifying Adjacent Action Discrepancy via Frequency-Selective Filtering for Skeleton-Based Action Segmentation [[paper](https://arxiv.org/abs/2603.24134)] [[code](https://github.com/HaoyuJi/SpecScalpel)]\n- OMG-Bench: A New Challenging Benchmark for Skeleton-based Online Micro Hand Gesture Recognition [[paper](https://arxiv.org/abs/2512.16727)]\n\n**ICLR**\n- Curvature-Guided Task Synergy for Skeleton based Temporal Action Segmentation [[paper](https://openreview.net/forum?id=Vgh30npuN3)]\n- Subspace Kernel Learning on Tensor Sequences [[paper](https://openreview.net/forum?id=kv22NbU2T2)]\n\n**AAAI**\n- FineTec: Fine-Grained Action Recognition under Temporal Corruption via Skeleton Decomposition and Sequence Completion [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/37838)] [[code](https://github.com/SmartDianLab/FineTec)]\n- Learning Dynamics as Feedback: An Adaptive Entropy Flow Dynamics Framework for Long-tailed Human Action Recognition [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/39226)] [[code](https://github.com/ddddddy1221/AEED)]\n- Few-Shot Precise Event Spotting via Unified Multi-Entity Graph and Distillation [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/37681)] [[code](https://github.com/LZYAndy/UMEG-Net)]\n- SUGAR: Learning Skeleton Representation with Visual-Motion Knowledge for Action Recognition [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/38852)]\n- Decomposing Prompts, Composing Actions: A Multi-Granularity Prompting Approach for Incremental Action Learning [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/37326)]\n\n**CVPRF**\n- Learning by Neighbor-Aware Semantics, Deciding by Open-form Flows: Towards Robust Zero-Shot Skeleton Action Recognition [[paper](https://arxiv.org/abs/2511.09388)] [[code](https://github.com/cseeyangchen/Flora)]\n\n**CVPRW**\n- BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket Sports [[paper](https://arxiv.org/abs/2502.21085)] [[code](https://github.com/Va6lue/BST-Badminton-Stroke-type-Transformer)]\n- SBF: An Effective Representation to Augment Skeleton for Video-based Human Action Recognition [[paper](https://arxiv.org/abs/2604.03590)]\n\n**ICPR**\n- CascadeFormer: A Family of Two-stage Cascading Transformers for Skeleton-based Human Action Recognition [[paper](https://arxiv.org/abs/2509.00692)] [[code](https://github.com/Yusen-Peng/CascadeFormer)]\n- SCALE: Semantic- and Confidence-Aware Conditional Variational Autoencoder for Zero-shot Skeleton-based Action Recognition [[paper](https://arxiv.org/abs/2604.02222)]\n\n**IJCV**\n- DeST: A Decoupled Spatio-Temporal Framework for Action Segmentation [[paper](https://link.springer.com/article/10.1007/s11263-026-02797-0)] [[code](https://github.com/lyhisme/DeST)]\n\n**TIP**\n- Attack-Augmented Mixing-Contrastive Skeletal Representation Learning [[paper](https://ieeexplore.ieee.org/abstract/document/11372607)] [[code](https://github.com/1xbq1/A2MC)]\n\n**TIFS**\n- Bones of Contention: Exploring Query-Efficient Attacks Against Skeleton Recognition Systems [[paper](https://arxiv.org/abs/2501.16843)]\n\n**TMM**\n- Ranking-based Self-Supervised Representation Learning for Skeleton-based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/11353928)]\n\n**TCSVT**\n- STAR++: Region-aware Conditional Semantics via Interpretable Side Information for Zero-Shot Skeleton Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/11339971)] [[code](https://github.com/cseeyangchen/STAR_pp)]\n- Dynamic Prompting Spatial Temporal Actor Transformer for Fine-grained Skeleton-based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/11328855)]\n- Constant-invariant Information Guided Augmented Spatiotemporal Networks for Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/11422982)]\n\n**PR**\n- RelPosGAR: Hierarchical relative position-aware interaction modeling for weakly supervised skeleton-based group activity recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0031320326002165)] [[code](https://github.com/li-lindong/RelPosGAR)]\n- Frequency-Aware Spatio-Temporal Topology Learning for Skeleton-Based Human Activity Recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0031320326001093)]\n- ST-VA-AR: Learning velocity-aware action representations with mixture of spatiotemporal attention [[paper](https://www.sciencedirect.com/science/article/pii/S0031320326001652)]\n\n**Neurocomputing**\n- FMFNet: A Faster Multimodal Fusion Network for action recognition via efficient modality compensation [[paper](https://www.sciencedirect.com/science/article/pii/S0925231226004881)]\n\n**arXiv papers**\n- Affinity Contrastive Learning for Skeleton-based Human Activity Understanding [[paper](https://arxiv.org/abs/2601.16694)] [[code](https://github.com/firework8/ACLNet)]\n- BHaRNet: Reliability-Aware Body-Hand Modality Expertized Networks for Fine-grained Skeleton Action Recognition [[paper](https://arxiv.org/abs/2601.00369)] [[code](https://github.com/VinnyCSY/BHaRNet)]\n- SkeFi: Cross-Modal Knowledge Transfer for Wireless Skeleton-Based Action Recognition [[paper](https://arxiv.org/abs/2601.12432)] [[code](https://github.com/Huang0035/Skefi)]\n- E2E-GNet: An End-to-End Skeleton-based Geometric Deep Neural Network for Human Motion Recognition [[paper](https://arxiv.org/abs/2603.02477)] [[code](https://github.com/ayodejimb/E2E-GNet)]\n- Less is More: Decoder-Free Masked Modeling for Efficient Skeleton Representation Learning [[paper](https://arxiv.org/abs/2603.10648)] [[code](https://github.com/KAIST-VICLab/SLiM)]\n- Variational Contrastive Learning for Skeleton-based Action Recognition [[paper](https://arxiv.org/abs/2601.07666)]\n- ASMa: Asymmetric Spatio-temporal Masking for Skeleton Action Representation Learning [[paper](https://arxiv.org/abs/2602.06251)]\n- Skarimva: Skeleton-based Action Recognition is a Multi-view Application [[paper](https://arxiv.org/abs/2602.23231)]\n- Skeleton-to-Image Encoding: Enabling Skeleton Representation Learning via Vision-Pretrained Models [[paper](https://arxiv.org/abs/2603.05963)]\n- Point-Supervised Skeleton-Based Human Action Segmentation [[paper](https://arxiv.org/abs/2603.06201)]\n- M3GCLR: Multi-View Mini-Max Infinite Skeleton-Data Game Contrastive Learning For Skeleton-Based Action Recognition [[paper](https://arxiv.org/abs/2603.09367)]\n- Severe Domain Shift in Skeleton-Based Action Recognition: A Study of Uncertainty Failure in Real-World Gym Environments [[paper](https://arxiv.org/abs/2603.15574)]\n- KGS-GCN: Enhancing Sparse Skeleton Sensing via Kinematics-Driven Gaussian Splatting and Probabilistic Topology for Action Recognition [[paper](https://arxiv.org/abs/2603.16943)]\n- Universal Skeleton Understanding via Differentiable Rendering and MLLMs [[paper](https://arxiv.org/abs/2603.18003)]\n- S3T-Former: A Purely Spike-Driven State-Space Topology Transformer for Skeleton Action Recognition [[paper](https://arxiv.org/abs/2603.18062)]\n- LLM Enhanced Action Recognition via Hierarchical Global-Local Skeleton-Language Model [[paper](https://arxiv.org/abs/2603.27103)]\n\n\n### 2025\n\n**CVPR**\n- Revealing Key Details to See Differences: A Novel Prototypical Perspective for Skeleton-based Action Recognition [[paper](https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Revealing_Key_Details_to_See_Differences_A_Novel_Prototypical_Perspective_CVPR_2025_paper.pdf)] [[code](https://github.com/firework8/ProtoGCN)]\n- Are Spatial-Temporal Graph Convolution Networks for Human Action Recognition Over-Parameterized? [[paper](https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_Are_Spatial-Temporal_Graph_Convolution_Networks_for_Human_Action_Recognition_Over-Parameterized_CVPR_2025_paper.pdf)] [[code](https://github.com/davelailai/Sparse-ST-GCN)]\n- Neuron: Learning Context-Aware Evolving Representations for Zero-Shot Skeleton Action Recognition [[paper](https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Neuron_Learning_Context-Aware_Evolving_Representations_for_Zero-Shot_Skeleton_Action_Recognition_CVPR_2025_paper.pdf)] [[code](https://github.com/cseeyangchen/Neuron)]\n- Heterogeneous Skeleton-Based Action Representation Learning [[paper](https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Heterogeneous_Skeleton-Based_Action_Representation_Learning_CVPR_2025_paper.pdf)]\n- Semantic-guided Cross-Modal Prompt Learning for Skeleton-based Zero-shot Action Recognition [[paper](https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_Semantic-guided_Cross-Modal_Prompt_Learning_for_Skeleton-based_Zero-shot_Action_Recognition_CVPR_2025_paper.pdf)]\n\n**ICCV**\n- Adaptive Hyper-Graph Convolution Network for Skeleton-based Human Action Recognition with Virtual Connections [[paper](https://openaccess.thecvf.com/content/ICCV2025/papers/Zhou_Adaptive_Hyper-Graph_Convolution_Network_for_Skeleton-based_Human_Action_Recognition_with_ICCV_2025_paper.pdf)] [[code](https://github.com/6UOOON9/Hyper-GCN)]\n- Frequency-Semantic Enhanced Variational Autoencoder for Zero-Shot Skeleton-based Action Recognition [[paper](https://openaccess.thecvf.com/content/ICCV2025/papers/Wu_Frequency-Semantic_Enhanced_Variational_Autoencoder_for_Zero-Shot_Skeleton-based_Action_Recognition_ICCV_2025_paper.pdf)] [[code](https://github.com/wenhanwu95/FS-VAE)]\n- Bridging the Skeleton-Text Modality Gap: Diffusion-Powered Modality Alignment for Zero-shot Skeleton-based Action Recognition [[paper](https://openaccess.thecvf.com/content/ICCV2025/papers/Do_Bridging_the_Skeleton-Text_Modality_Gap_Diffusion-Powered_Modality_Alignment_for_Zero-shot_ICCV_2025_paper.pdf)] [[code](https://github.com/KAIST-VICLab/TDSM)]\n- Bridging Class Imbalance and Partial Labeling via Spectral-Balanced Energy Propagation for Skeleton-based Action Recognition [[paper](https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_Bridging_Class_Imbalance_and_Partial_Labeling_via_Spectral-Balanced_Energy_Propagation_ICCV_2025_paper.pdf)] [[code](https://github.com/ydanwang/SpeLER)]\n- Privacy-centric Deep Motion Retargeting for Anonymization of Skeleton-Based Motion Visualization [[paper](https://openaccess.thecvf.com/content/ICCV2025/papers/Carr_Privacy-centric_Deep_Motion_Retargeting_for_Anonymization_of_Skeleton-Based_Motion_Visualization_ICCV_2025_paper.pdf)] [[code](https://github.com/Thomasc33/Privacy-Retargeting)]\n- Hierarchical-aware Orthogonal Disentanglement Framework for Fine-grained Skeleton-based Action Recognition [[paper](https://openaccess.thecvf.com/content/ICCV2025/papers/Chang_Hierarchical-aware_Orthogonal_Disentanglement_Framework_for_Fine-grained_Skeleton-based_Action_Recognition_ICCV_2025_paper.pdf)]\n- Towards Efficient General Feature Prediction in Masked Skeleton Modeling [[paper](https://openaccess.thecvf.com/content/ICCV2025/papers/Sun_Towards_Efficient_General_Feature_Prediction_in_Masked_Skeleton_Modeling_ICCV_2025_paper.pdf)]\n- Sequential Keypoint Density Estimator: An Overlooked Baseline of Skeleton-based Video Anomaly Detection [[paper](https://openaccess.thecvf.com/content/ICCV2025/papers/Delic_Sequential_keypoint_density_estimator_an_overlooked_baseline_of_skeleton-based_video_ICCV_2025_paper.pdf)]\n- Skeleton Motion Words for Unsupervised Skeleton-Based Temporal Action Segmentation [[paper](https://openaccess.thecvf.com/content/ICCV2025/papers/Gokay_Skeleton_Motion_Words_for_Unsupervised_Skeleton-Based_Temporal_Action_Segmentation_ICCV_2025_paper.pdf)]\n- DuoCLR: Dual-Surrogate Contrastive Learning for Skeleton-based Human Action Segmentation [[paper](https://openaccess.thecvf.com/content/ICCV2025/papers/Tian_DuoCLR_Dual-Surrogate_Contrastive_Learning_for_Skeleton-based_Human_Action_Segmentation_ICCV_2025_paper.pdf)]\n\n**NeurIPS** \n- Boosting Skeleton-based Zero-Shot Action Recognition with Training-Free Test-Time Adaptation [[paper](https://openreview.net/forum?id=wjXKFrUFzA)] [[code](https://github.com/Alchemist0754/Skeleton-Cache)]\n- Doodle to Detect: A Goofy but Powerful Approach to Skeleton-based Hand Gesture Recognition [[paper](https://openreview.net/forum?id=u8SXX5ITE6)] [[code](https://github.com/capableofanything/SKETCH)]\n- OSKAR: Omnimodal Self-supervised Knowledge Abstraction and Representation [[paper](https://openreview.net/forum?id=LWuhOoHpo5)]\n\n**ICLR**\n- TASAR: Transfer-based Attack on Skeletal Action Recognition [[paper](https://arxiv.org/pdf/2409.02483)] [[code](https://github.com/qkicen/Skeleton-Robustness-Benchmark)]\n\n**AAAI**\n- USDRL: Unified Skeleton-Based Dense Representation Learning with Multi-Grained Feature Decorrelation [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/32899)] [[code](https://github.com/wengwanjiang/USDRL)]\n- SKI Models: Skeleton Induced Vision-Language Embeddings for Understanding Activities of Daily Living [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/32744)] [[code](https://github.com/thearkaprava/SKI-Models)]\n- VA-AR: Learning Velocity-Aware Action Representations with Mixture of Window Attention [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/32894)]\n- Skeleton-based Action Recognition with Non-linear Dependency Modeling and Hilbert-Schmidt Independence Criterion [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/32201)]\n- Rethinking Masked Data Reconstruction Pretraining for Strong 3D Action Representation Learning [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/32324)]\n- Stitch, Contrast, and Segment: Learning a Human Action Segmentation Model Using Trimmed Skeleton Videos [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/32792)]\n\n**ACM MM**\n- Motion Matters: Motion-guided Modulation Network for Skeleton-based Micro-Action Recognition [[paper](https://arxiv.org/abs/2507.21977)] [[code](https://github.com/momiji-bit/MMN)]\n- Signal-SGN: A Spiking Graph Convolutional Network for Skeleton Action Recognition via Learning Temporal-Frequency Dynamics [[paper](https://dl.acm.org/doi/abs/10.1145/3746027.3755246)] [[code](https://github.com/zhengnaichuan2022/Signal-SGN)]\n- Kinematic Enhanced Hypergraph Convolutional Network for Skeleton-based Human Action Recognition with LLM Training Guides [[paper](https://dl.acm.org/doi/abs/10.1145/3746027.3755538)]\n- Skeleton Compression and Complementary Enhanced Fusion Under Branch-Stage Supervision for Human Action Recognition [[paper](https://dl.acm.org/doi/abs/10.1145/3746027.3755690)]\n\n**ICCVW**\n- Learning Robust Aligned Representations Across Multiple Visual Modalities in Human Action Recognition [[paper](https://openaccess.thecvf.com/content/ICCV2025W/SAUAFG/papers/Lerch_Learning_Robust_Aligned_Representations_Across_Multiple_Visual_Modalities_in_Human_ICCVW_2025_paper.pdf)]\n\n**BMVC**\n- Multimodal Feature Collaboration and Fusion for Fine-grained Action Recognition [[paper](https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_284/paper.pdf)]\n\n**WACV**\n- Autoregressive Adaptive Hypergraph Transformer for Skeleton-based Activity Recognition [[paper](https://arxiv.org/abs/2411.05692)] [[code](https://github.com/rayabhisek123/AutoregAd-HGformer)]\n\n**ICIP**\n- MFA-Net: Motion Field Adaptive Network for Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/11084688)]\n- Stable-Invertible Graph Convolutional Networks for Label-Efficient Skeleton-Based Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/11084667)]\n\n**ICASSP**\n- Auxiliary Tasks Benefit Skeleton-based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10887882)]\n- Hybrid Spatial-Frequency Attention Network For Fine-Grained Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10890541)]\n- SkeletonMix: A Mixup-Based Data Augmentation Framework for Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10888125)]\n- Dual Multi-Scale GCN with Deformable Temporal Kernel for Skeleton-based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10890725)]\n- Joint-Wise Distributed Perception Graph Convolutional Network for Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10888560)]\n- Multi-scale Graph Convolution with Corrective Contrastive Learning for Skeleton-based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10890514)]\n- Reenvisioning Skeleton-based Action Recognition Through the Lens of NLP [[paper](https://ieeexplore.ieee.org/abstract/document/10888571)]\n- PASTD: Progressive Augmentation and Spatiotemporal Decoupling Contrastive Learning for Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10890079)]\n\n**IROS**\n- Body-Hand Modality Expertized Networks with Cross-attention for Fine-grained Skeleton Action Recognition [[paper](https://arxiv.org/abs/2503.14960)] [[code](https://github.com/VinnyCSY/BHaRNet)]\n- G3CN: Gaussian Topology Refinement Gated Graph Convolutional Network for Skeleton-Based Action Recognition [[paper](https://arxiv.org/abs/2509.07335)] [[code](https://github.com/CyanSea123/G3CN-Gaussian-Topology)]\n- MaskSem: Semantic-Guided Masking for Learning 3D Hybrid High-Order Motion Representation [[paper](https://arxiv.org/abs/2508.12948)]\n\n**TPAMI**\n- Heatmap Pooling Network for Action Recognition from RGB Videos [[paper](https://ieeexplore.ieee.org/abstract/document/11278750)] [[code](https://github.com/liujf69/HPNet-Action)]\n- Foundation Model for Skeleton-Based Human Action Understanding [[paper](https://ieeexplore.ieee.org/abstract/document/11130651)] [[code](https://github.com/wengwanjiang/FoundSkelModel)]\n- Hulk: A Universal Knowledge Translator for Human-Centric Tasks [[paper](https://ieeexplore.ieee.org/abstract/document/10930828)] [[code](https://github.com/OpenGVLab/Hulk)]\n- Self-Supervised Skeleton Representation Learning via Actionlet Contrast and Reconstruct [[paper](https://ieeexplore.ieee.org/abstract/document/11123705)] [[code](https://github.com/LanglandsLin/ActCLR)]\n\n**IJCV**\n- I2MD: 3D Action Representation Learning with Inter- and Intra-modal Mutual Distillation [[paper](https://link.springer.com/article/10.1007/s11263-025-02415-5)]\n\n**TIP**\n- Expressive Keypoints for Skeleton-Based Action Recognition via Progressive Skeleton Evolution [[paper](https://ieeexplore.ieee.org/document/11258079)] [[code](https://github.com/YijieYang23/PSE-GCN)]\n- Zero-shot Skeleton-based Action Recognition with Prototype-guided Feature Alignment [[paper](https://ieeexplore.ieee.org/abstract/document/11083680)] [[code](https://github.com/kaai520/PGFA)]\n- Text-Derived Relational Graph-Enhanced Network for Skeleton-Based Action Segmentation [[paper](https://ieeexplore.ieee.org/abstract/document/11220247)] [[code](https://github.com/HaoyuJi/TRG-Net)]\n- Momentum Contrastive Teacher for Semi-Supervised Skeleton Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10820022)]\n- Client-Unbiased Skeletal Action Recognizer in Federated Learning [[paper](https://ieeexplore.ieee.org/abstract/document/11079817)]\n- Informative Sample Selection Model for Skeleton-based Action Recognition with Limited Training Samples [[paper](https://ieeexplore.ieee.org/abstract/document/11235602)]\n\n**TMM**\n- Language Knowledge-Assisted Representation Learning for Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10891636)] [[code](https://github.com/damnull/lagcn)]\n- SkeletonX: Data-Efficient Skeleton-based Action Recognition via Cross-sample Feature Aggregation [[paper](https://arxiv.org/pdf/2504.11749)] [[code](https://github.com/zzysteve/SkeletonX)]\n- Multi-View Knowledge Guided Semantic Prototype Learning for Generalized Zero-Shot Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/11194256)] [[code](https://github.com/EHZ9NIWI7/AMSF-GZSSAR)]\n- Contrastive Feedback Vision-Language for 3D Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10855504)]\n- An Information Compensation Framework for Zero-Shot Skeleton-based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10891402)]\n- Prompt-Guided Prototype-Aware Commonality and Discrimination Learning for Zero-Shot Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/11086379)]\n- Action-Responsive Contrastive Network for Fine-Grained Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/11147133)]\n\n**TCSVT**\n- TDSN-GCN: Transformerify Overall Structure Decaying Static Graph Embedding NAS-guided GCN for Skeleton Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/11176951)] [[code](https://github.com/vvhj/TDSN-GCN)]\n- Asymmetric Context-guided Adaptive Alignment Network for Skeleton-based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10843295)]\n- Unsupervised Feature Enrichment and Fidelity Preservation Learning Framework for Skeleton-based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10879058)]\n- Robust 2D Skeleton Action Recognition via Decoupling and Distilling 3D Latent Features [[paper](https://ieeexplore.ieee.org/abstract/document/10972084)]\n\n**PR**\n- A Generically Contrastive Spatiotemporal Representation Enhancement for 3D Skeleton Action Recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0031320325001815)] [[code](https://github.com/zhshj0110/CSRE)]\n- Dual-decoder collaborative learning with multi-hybrid view augmentation for self-supervised 3D action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S003132032501012X)] [[code](https://github.com/Yingfei-Wu/DDC)]\n- Zero-Shot Skeleton-based Action Recognition with Dual Visual-Text Alignment [[paper](https://www.sciencedirect.com/science/article/abs/pii/S0031320325010039)] [[code](https://github.com/jidongkuang/DVTA)]\n- SAM-Net: Semantic-assisted multimodal network for action recognition in RGB-D videos [[paper](https://www.sciencedirect.com/science/article/pii/S0031320325003851)]\n- Skeleton-prompt: A cross-dataset transfer learning approach for skeleton action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S003132032500545X)]\n- THTFormer: Topology-adaptive hypergraph transformer network for skeleton-based action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S003132032500785X)]\n- Hierarchical kernel decoupling for graph convolution: Enhancing skeleton-based action recognition through structured representation [[paper](https://www.sciencedirect.com/science/article/pii/S0031320325013159)]\n- RL-GTN: A reinforced divergence-optimized graph transformer network for skeleton-based action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0031320325013445)]\n\n**Neurocomputing**\n- A robust two-stage framework for human skeleton action recognition with GAIN and masked autoencoder [[paper](https://www.sciencedirect.com/science/article/pii/S0925231225001055)] [[code](https://github.com/GD1201/A-two-stage-network-for-action-recognition)]\n- EPAM-Net: An Efficient Pose-driven Attention-guided Multimodal Network for Video Action Recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0925231225004539)] [[code](https://github.com/ahmed-nady/Multimodal-Action-Recognition)]\n- Dstsa-gcn: Advancing skeleton-based gesture recognition with semantic-aware spatio-temporal topology modeling [[paper](https://www.sciencedirect.com/science/article/abs/pii/S0925231225007386)] [[code](https://github.com/HuCui2022/DSTSA-GCN_Gesture)]\n- High-Performance Inference Graph Convolutional Networks for Skeleton-Based Action Recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0925231225017503)] [[code](https://github.com/lizaowo/HPI-GCN)]\n- MK-SGN: A spiking graph convolutional network with multimodal fusion and knowledge distillation for skeleton-based action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0925231225024683)] [[code](https://github.com/zhengnaichuan2022/MK-SGN)]\n- SHoTGCN: Spatial high-order temporal GCN for skeleton-based action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0925231225003698)]\n- Local and Global Spatial-Temporal Transformer for skeleton-based action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0925231225004928)]\n- Exploring interaction: Inner-outer spatial–temporal transformer for skeleton-based mutual action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0925231225006794)]\n- Skeleton-based action recognition through dual-granularity feature fusion with self-adapting graph convolution and multi-scale temporal convolution [[paper](https://www.sciencedirect.com/science/article/pii/S0925231225009336)]\n- A dual-stream GCN-based action recognition framework using trustworthy fusion decision from different skeleton descriptors [[paper](https://www.sciencedirect.com/science/article/pii/S0925231225021472)]\n- Disentangled adaptive multi-dimensional dynamic graph convolutional network for skeleton-based action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0925231225023653)]\n- Integrating spatio-temporal modeling of RGB video with multi-stream skeleton representations for advanced human action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0925231225024634)]\n- Masked reconstruction model of latent space vector quantization for human skeleton-based action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0925231225027985)]\n\n**arXiv papers**\n- SkeletonAgent: An Agentic Interaction Framework for Skeleton-based Action Recognition [[paper](https://arxiv.org/abs/2511.22433)] [[code](https://github.com/firework8/SkeletonAgent)]\n- Spatio-Temporal Joint Density Driven Learning for Skeleton-Based Action Recognition [[paper](https://arxiv.org/abs/2505.23012)] [[code](https://github.com/ShanakaRG/STJD-Spatio-Temporal-Joint-Density-Driven-Learning-for-Skeleton-Based-Action-Recognition)]\n- UniSTFormer: Unified Spatio-Temporal Lightweight Transformer for Efficient Skeleton-Based Action Recognition [[paper](https://arxiv.org/abs/2508.08944)] [[code](https://github.com/wenhanwu95/FreqMixFormer/tree/main/UniSTFormer)]\n- MS-CLR: Multi-Skeleton Contrastive Learning for Human Action Recognition [[paper](https://arxiv.org/abs/2508.14889)] [[code](https://github.com/3Dwe-ai/ms-clr)]\n- LSTC-MDA: A Unified Framework for Long-Short Term Temporal Convolution and Mixed Data Augmentation in Skeleton-Based Action Recognition [[paper](https://arxiv.org/abs/2509.14619)] [[code](https://github.com/xiaobaoxia/LSTC-MDA)]\n- DoGCLR: Dominance-Game Contrastive Learning Network for Skeleton-Based Action Recognition [[paper](https://arxiv.org/abs/2511.14179)] [[code](https://github.com/Ixiaohuihuihui/DoGCLR)]\n- TSkel-Mamba: Temporal Dynamic Modeling via State Space Model for Human Skeleton-based Action Recognition [[paper](https://arxiv.org/abs/2512.11503)] [[code](https://github.com/ryannus2025-ai/TSkel-Mamba)]\n- DynaPURLS: Dynamic Refinement of Part-aware Representations for Skeleton-based Zero-Shot Action Recognition [[paper](https://arxiv.org/abs/2512.11941)] [[code](https://github.com/Alchemist0754/DynaPURLS)]\n- Evolving Skeletons: Motion Dynamics in Action Recognition [[paper](https://arxiv.org/abs/2501.02593)]\n- HFGCN: Hypergraph Fusion Graph Convolutional Networks for Skeleton-Based Action Recognition [[paper](https://arxiv.org/abs/2501.11007)]\n- HyLiFormer: Hyperbolic Linear Attention for Skeleton-based Human Action Recognition [[paper](https://arxiv.org/abs/2502.05869)]\n- SNN-Driven Multimodal Human Action Recognition via Event Camera and Skeleton Data Fusion [[paper](https://arxiv.org/abs/2502.13385)]\n- CoCoDiff: Diversifying Skeleton Action Features via Coarse-Fine Text-Co-Guided Latent Diffusion [[paper](https://arxiv.org/abs/2504.21266)]\n- Variational Graph Convolutional Neural Networks [[paper](https://arxiv.org/abs/2507.01699)]\n- Action Hints: Semantic Typicality and Context Uniqueness for Generalizable Skeleton-based Video Anomaly Detection [[paper](https://arxiv.org/abs/2509.11058)]\n- Parts-Mamba: Augmenting Joint Context with Part-Level Scanning for Occluded Human Skeleton [[paper](https://arxiv.org/abs/2511.16860)]\n- Label-Efficient Skeleton-based Recognition with Stable-Invertible Graph Convolutional Networks [[paper](https://arxiv.org/abs/2511.17345)]\n- Active Learning for GCN-based Action Recognition [[paper](https://arxiv.org/abs/2511.21625)]\n- Skeleton-Snippet Contrastive Learning with Multiscale Feature Fusion for Action Localization [[paper](https://arxiv.org/abs/2512.16504)]\n- Multimodal Skeleton-Based Action Representation Learning via Decomposition and Composition [[paper](https://arxiv.org/abs/2512.21064)]\n- Patch as Node: Human-Centric Graph Representation Learning for Multimodal Action Recognition [[paper](https://arxiv.org/abs/2512.21916)]\n- Signal-SGN++: Topology-Enhanced Time-Frequency Spiking Graph Network for Skeleton-Based Action Recognition [[paper](https://arxiv.org/abs/2512.22214)]\n\n\n### 2024\n\n**CVPR**\n- BlockGCN: Redefine Topology Awareness for Skeleton-Based Action Recognition [[paper](https://openaccess.thecvf.com/content/CVPR2024/papers/Zhou_BlockGCN_Redefine_Topology_Awareness_for_Skeleton-Based_Action_Recognition_CVPR_2024_paper.pdf)] [[code](https://github.com/ZhouYuxuanYX/BlockGCN)] [🔥] [⭐]\n- Just Add π! Pose Induced Video Transformers for Understanding Activities of Daily Living [[paper](https://openaccess.thecvf.com/content/CVPR2024/papers/Reilly_Just_Add__Pose_Induced_Video_Transformers_for_Understanding_Activities_CVPR_2024_paper.pdf)] [[code](https://github.com/dominickrei/pi-vit)]\n- Part-aware Unified Representation of Language and Skeleton for Zero-shot Action Recognition [[paper](https://openaccess.thecvf.com/content/CVPR2024/papers/Zhu_Part-aware_Unified_Representation_of_Language_and_Skeleton_for_Zero-shot_Action_CVPR_2024_paper.pdf)] [[code](https://github.com/azzh1/PURLS)]\n- Skeleton-in-Context: Unified Skeleton Sequence Modeling with In-Context Learning [[paper](https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_Skeleton-in-Context_Unified_Skeleton_Sequence_Modeling_with_In-Context_Learning_CVPR_2024_paper.pdf)] [[code](https://github.com/fanglaosi/Skeleton-in-Context)]\n- LLMs are Good Action Recognizers [[paper](https://openaccess.thecvf.com/content/CVPR2024/papers/Qu_LLMs_are_Good_Action_Recognizers_CVPR_2024_paper.pdf)] [🔥]\n- MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning [[paper](https://openaccess.thecvf.com/content/CVPR2024/papers/Abdelfattah_MaskCLR_Attention-Guided_Contrastive_Learning_for_Robust_Action_Representation_Learning_CVPR_2024_paper.pdf)]\n\n**ECCV**\n- SkateFormer: Skeletal-Temporal Transformer for Human Action Recognition [[paper](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/05796.pdf)] [[code](https://github.com/KAIST-VICLab/SkateFormer)] [🔥] [⭐]\n- MacDiff: Unified Skeleton Modeling with Masked Conditional Diffusion [[paper](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/03727.pdf)] [[code](https://github.com/LehongWu/MacDiff)]\n- SA-DVAE: Improving Zero-Shot Skeleton-Based Action Recognition by Disentangled Variational Autoencoders [[paper](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/02508.pdf)] [[code](https://github.com/pha123661/SA-DVAE)]\n- On the Utility of 3D Hand Poses for Action Recognition [[paper](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/01025.pdf)] [[code](https://github.com/s-shamil/HandFormer)]\n- Skeleton-based Group Activity Recognition via Spatial-Temporal Panoramic Graph [[paper](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/07657.pdf)] [[code](https://github.com/mgiant/MP-GCN)]\n- VSViG: Real-time Video-based Seizure Detection via Skeleton-based Spatiotemporal ViG [[paper](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/10768.pdf)] [[code](https://github.com/xuyankun/VSViG)]\n- Language-Assisted Skeleton Action Understanding for Skeleton-Based Temporal Action Segmentation [[paper](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/07145.pdf)] [[code](https://github.com/HaoyuJi/LaSA)]\n- Idempotent Unsupervised Representation Learning for Skeleton-Based Action Recognition [[paper](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/03717.pdf)]\n- S-JEPA: A Joint Embedding Predictive Architecture for Skeletal Action Recognition [[paper](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/04755.pdf)]\n- CrossGLG: LLM Guides One-shot Skeleton-based 3D Action Recognition in a Cross-level Manner [[paper](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/03008.pdf)]\n- Towards Physical World Backdoor Attacks against Skeleton Action Recognition [[paper](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/06445.pdf)]\n\n**NeurIPS** \n- CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action Recognition [[paper](https://arxiv.org/pdf/2410.07153)] [[code](https://github.com/Necolizer/CHASE)]\n- Recovering Complete Actions for Cross-dataset Skeleton Action Recognition [[paper](https://arxiv.org/pdf/2410.23641)] [[code](https://github.com/HanchaoLiu/Recover-and-Resample)]\n\n**AAAI**\n- Dynamic Semantic-Based Spatial Graph Convolution Network for Skeleton-Based Human Action Recognition [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/28440)] [[code](https://github.com/davelailai/DS-GCN)] [🔥]\n- SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-supervised Skeleton-based Action Recognition [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/28409)] [[code](https://github.com/cong-wu/SCD-Net)]\n- Navigating Open Set Scenarios for Skeleton-based Action Recognition [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/28247)] [[code](https://github.com/KPeng9510/OS-SAR)]\n- Behavioral Recognition of Skeletal Data Based on Targeted Dual Fusion Strategy [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/28517)]\n- Spatio-Temporal Fusion for Human Action Recognition via Joint Trajectory Graph [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/28590)]\n\n**ACM MM**\n- Frequency Guidance Matters: Skeletal Action Recognition by Frequency-Aware Mixed Transformer [[paper](https://arxiv.org/pdf/2407.12322)] [[code](https://github.com/wenhanwu95/FreqMixFormer)]\n- Multi-Modality Co-Learning for Efficient Skeleton-based Action Recognition [[paper](https://arxiv.org/pdf/2407.15706)] [[code](https://github.com/liujf69/MMCL-Action)]\n- Fine-Grained Side Information Guided Dual-Prompts for Zero-Shot Skeleton Action Recognition [[paper](https://arxiv.org/pdf/2404.07487)] [[code](https://github.com/cseeyangchen/STAR)]\n\n**IJCAI**\n- Shap-Mix: Shapley Value Guided Mixing for Long-Tailed Skeleton Based Action Recognition [[paper](https://arxiv.org/pdf/2407.12312)] [[code](https://github.com/JHang2020/Shap-Mix)]\n\n**CVPRW**\n- Efficient Skeleton-Based Action Recognition for Real-Time Embedded Systems [[paper](https://openaccess.thecvf.com/content/CVPR2024W/MAI/papers/Noor_Efficient_Skeleton-Based_Action_Recognition_for_Real-Time_Embedded_Systems_CVPRW_2024_paper.pdf)]\n\n**ECCVW**\n- HybridFormer: Bridging Local and Global Spatio-Temporal Dynamics for Efficient Skeleton-Based Action Recognition [[paper](https://link.springer.com/chapter/10.1007/978-3-031-91575-8_2)]\n\n**ICPR**\n- Mask and Compress: Efficient Skeleton-based Action Recognition in Continual Learning [[paper](https://arxiv.org/pdf/2407.01397)] [[code](https://github.com/Sperimental3/CHARON)]\n- Spatio-Temporal Domain-Aware Network for Skeleton-Based Action Representation Learning [[paper](https://link.springer.com/chapter/10.1007/978-3-031-78110-0_10)]\n- EchoGCN: An Echo Graph Convolutional Network for Skeleton-Based Action Recognition [[paper](https://link.springer.com/chapter/10.1007/978-3-031-78354-8_16)]\n- Semi-structured Pruning of Graph Convolutional Networks for Skeleton-Based Recognition [[paper](https://link.springer.com/chapter/10.1007/978-3-031-78166-7_25)]\n- Hybrid Human Action Anomaly Detection Based on Lightweight GNNs and Machine Learning [[paper](https://link.springer.com/chapter/10.1007/978-3-031-78110-0_14)]\n\n**ICIP**\n- Hierarchical Vertex-Wise Intensification Graph Convolution for Skeleton-Based Activity Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10647399)]\n- Cross-Action Cross-Subject Skeleton Action Recognition Via Simultaneous Action-Subject Learning With Two-Step Feature Removal [[paper](https://ieeexplore.ieee.org/abstract/document/10647253)]\n\n**ICASSP**\n- Elevating Skeleton-Based Action Recognition with Efficient Multi-Modality Self-Supervision [[paper](https://arxiv.org/pdf/2309.12009.pdf)] [[code](https://github.com/desehuileng0o0/IKEM)]\n- Wavelet-Decoupling Contrastive Enhancement Network for Fine-Grained Skeleton-Based Action Recognition [[paper](https://arxiv.org/pdf/2402.02210.pdf)]\n- A Novel Contrastive Diffusion Graph Convolutional Network for Few-Shot Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10447668)]\n\n**IROS**\n- Skeleton-Based Human Action Recognition with Noisy Labels [[paper](https://arxiv.org/pdf/2403.09975)] [[code](https://github.com/xuyizdby/NoiseEraSAR)]\n\n**ICMEW**\n- HDBN: A Novel Hybrid Dual-branch Network for Robust Skeleton-based Action Recognition [[paper](https://arxiv.org/pdf/2404.15719.pdf)] [[code](https://github.com/liujf69/ICMEW2024-Track10)]\n\n**TPAMI**\n- InfoGCN++: Learning Representation by Predicting the Future for Online Skeleton-based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10694798)] [[code](https://github.com/stnoah1/infogcn2)]\n- One-Shot Action Recognition via Multi-Scale Spatial-Temporal Skeleton Matching [[paper](https://ieeexplore.ieee.org/abstract/document/10428035)]\n\n**IJCV**\n- View-invariant Skeleton Action Representation Learning via Motion Retargeting [[paper](https://link.springer.com/article/10.1007/s11263-023-01967-8)] [[code](https://github.com/YangDi666/UNIK)]\n\n**TIP**\n- DeGCN: Deformable Graph Convolutional Networks for Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10478824)] [[code](https://github.com/WoominM/DeGCN_pytorch)] [🔥]\n- SelfGCN: Graph Convolution Network With Self-Attention for Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10618962)] [[code](https://github.com/SunPengP/SelfGCN)]\n- Dynamic Semantic-based Spatial-Temporal Graph Convolution Network for Skeleton-based Human Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10758404)] [[code](https://github.com/davelailai/DS-STGCN)]\n- Mutual Information Driven Equivariant Contrastive Learning for 3D Action Representation Learning [[paper](https://ieeexplore.ieee.org/abstract/document/10462918)]\n- Multi-View Time-Series Hypergraph Neural Network for Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10517892)]\n\n**TMM**\n- Leveraging Enriched Skeleton Representation with Multi-relational Metrics for Few-shot Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10814069)] [[code](https://github.com/jinjinggu00/ESR-MM)]\n- Vision-Language Meets the Skeleton: Progressively Distillation with Cross-Modal Knowledge for 3D Action Representation Learning [[paper](https://arxiv.org/pdf/2405.20606)]\n- Adaptive Pitfall: Exploring the Effectiveness of Adaptation in Skeleton-based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10814651)]\n- Localized Linear Temporal Dynamics for Self-supervised Skeleton Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10539295)]\n- Hierarchical Aggregated Graph Neural Network for Skeleton-based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10598383)]\n- GCN-based Multi-modality Fusion Network for Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10814090)]\n\n**TCSVT**\n- SiT-MLP: A Simple MLP with Point-wise Topology Feature Learning for Skeleton-based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10495051)] [[code](https://github.com/Eezekiel/SiT-MLP)]\n- Asynchronous Joint-based Temporal Pooling for Skeleton-based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10685538)] [[code](https://github.com/ShanakaRG/AJTP)]\n- Glimpse and Zoom: Spatio-Temporal Focused Dynamic Network for Skeleton-based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10415064)]\n- Multi-scale Structural Graph Convolutional Network for Skeleton-based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10464339)]\n- Decoupled Knowledge Embedded Graph Convolutional Network for Skeleton-based Human Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10526294)]\n- Global and Local Contrastive Learning for Self-supervised Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10551297)]\n- Motion-Aware Mask Feature Reconstruction for Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10562342)]\n- Enhancing Skeleton-Based Action Recognition with Language Descriptions from Pre-trained Large Multimodal Models [[paper](https://ieeexplore.ieee.org/abstract/document/10742343)]\n- DSDC-GCN: Decoupled Static-Dynamic Co-occurrence Graph Convolutional Networks for Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10742428)]\n\n**TNNLS**\n- Language-Guided 3-D Action Feature Learning Without Ground-Truth Sample Class Label [[paper](https://ieeexplore.ieee.org/abstract/document/10555120)] [[code](https://github.com/tangent-T/W3AMT)]\n- GRA: Graph Representation Alignment for Semi-Supervised Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10398229)]\n- Multi-Dimensional Refinement Graph Convolutional Network with Robust Decouple Loss for Fine-Grained Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10499829)]\n\n**PR**\n- Improving self-supervised action recognition from extremely augmented skeleton sequences [[paper](https://www.sciencedirect.com/science/article/pii/S0031320324000840)] [[code](https://github.com/Levigty/AimCLR-v2)]\n- Spatiotemporal Progressive Inward-Outward Aggregation Network for skeleton-based action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S003132032400013X)]\n- Understanding the vulnerability of skeleton-based Human Activity Recognition via black-box attack [[paper](https://www.sciencedirect.com/science/article/pii/S0031320324003157)]\n\n**Neurocomputing**\n- A motion-aware and temporal-enhanced Spatial–Temporal Graph Convolutional Network for skeleton-based human action segmentation [[paper](https://www.sciencedirect.com/science/article/pii/S0925231224002534)] [[code](https://github.com/11yxk/openpack)]\n- Skeleton-OOD: An end-to-end skeleton-based model for robust out-of-distribution human action detection [[paper](https://www.sciencedirect.com/science/article/pii/S0925231224019295)] [[code](https://github.com/YilliaJing/Skeleton-OOD)]\n- Independent Dual Graph Attention Convolutional Network for skeleton-based action recognition [[paper](https://www.sciencedirect.com/science/article/abs/pii/S0925231224002674)]\n- Representation modeling learning with multi-domain decoupling for unsupervised skeleton-based action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0925231224002662)]\n- Multi-scale sampling attention graph convolutional networks for skeleton-based action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0925231224008579)]\n- Modeling the skeleton-language uncertainty for 3D action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0925231224011974)]\n- Prompt-supervised dynamic attention graph convolutional network for skeleton-based action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0925231224013948)]\n- Language-guided temporal primitive modeling for skeleton-based action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0925231224014073)]\n\n**arXiv papers**\n- AutoGCN-Toward Generic Human Activity Recognition With Neural Architecture Search [[paper](https://arxiv.org/abs/2402.01313)] [[code](https://github.com/DeepInMotion/AutoGCN)]\n- Graph in Graph Neural Network [[paper](https://arxiv.org/abs/2407.00696)] [[code](https://github.com/wangjs96/Graph-in-Graph-Neural-Network)]\n- Language Supervised Human Action Recognition with Salient Fusion: Construction Worker Action Recognition as a Use Case [[paper](https://arxiv.org/abs/2410.01962)] [[code](https://github.com/VCEET/ConstAct_HAR_SFU)]\n- GCN-DevLSTM: Path Development for Skeleton-Based Action Recognition [[paper](https://arxiv.org/abs/2403.15212)] [[code](https://github.com/DeepIntoStreams/GCN-DevLSTM)]\n- Synchronized and Fine-Grained Head for Skeleton-Based Ambiguous Action Recognition [[paper](https://arxiv.org/abs/2412.14833)] [[code](https://github.com/HaoHuang2003/SFHead)]\n- Active Generation Network of Human Skeleton for Action Recognition [[paper](https://arxiv.org/abs/2401.17086)] [[code](https://github.com/imustwangxin/active-generation-network)]\n- STARS: Self-supervised Tuning for 3D Action Recognition in Skeleton Sequences [[paper](https://arxiv.org/abs/2407.10935)] [[code](https://github.com/TaatiTeam/STARS)]\n- Spatial Hierarchy and Temporal Attention Guided Cross Masking for Self-supervised Skeleton-based Action Recognition [[paper](https://arxiv.org/abs/2409.17951)] [[code](https://github.com/YinxPeng/HA-CM-main)]\n- Topological Symmetry Enhanced Graph Convolution for Skeleton-Based Action Recognition [[paper](https://arxiv.org/abs/2411.12560)]\n- Skeleton2vec: A Self-supervised Learning Framework with Contextualized Target Representations for Skeleton Sequence [[paper](https://arxiv.org/abs/2401.00921)]\n- Multi-Scale Spatial-Temporal Self-Attention Graph Convolutional Networks for Skeleton-based Action Recognition [[paper](https://arxiv.org/abs/2404.02624)]\n- Simba: Mamba augmented U-ShiftGCN for Skeletal Action Recognition in Videos [[paper](https://arxiv.org/abs/2404.07645)]\n- An Improved Graph Pooling Network for Skeleton-Based Action Recognition [[paper](https://arxiv.org/abs/2404.16359)]\n- Language-Assisted Human Part Motion Learning for Skeleton-Based Temporal Action Segmentation [[paper](https://arxiv.org/abs/2410.06353)]\n- SkelMamba: A State Space Model for Efficient Skeleton Action Recognition of Neurological Disorders [[paper](https://arxiv.org/abs/2411.19544)]\n\n\n### 2023\n\n**CVPR**\n- Learning Discriminative Representations for Skeleton Based Action Recognition [[paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Learning_Discriminative_Representations_for_Skeleton_Based_Action_Recognition_CVPR_2023_paper.pdf)] [[code](https://github.com/zhysora/FR-Head)] [🔥] [⭐]\n- Neural Koopman Pooling: Control-Inspired Temporal Dynamics Encoding for Skeleton-Based Action Recognition [[paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Neural_Koopman_Pooling_Control-Inspired_Temporal_Dynamics_Encoding_for_Skeleton-Based_Action_CVPR_2023_paper.pdf)] [[code](https://github.com/Infinitywxh/Neural_Koopman_pooling)]\n- Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition [[paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_Actionlet-Dependent_Contrastive_Learning_for_Unsupervised_Skeleton-Based_Action_Recognition_CVPR_2023_paper.pdf)] [[code](https://github.com/LanglandsLin/ActCLR)] [🔥]\n- HaLP: Hallucinating Latent Positives for Skeleton-based Self-Supervised Learning of Actions [[paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Shah_HaLP_Hallucinating_Latent_Positives_for_Skeleton-Based_Self-Supervised_Learning_of_Actions_CVPR_2023_paper.pdf)] [[code](https://github.com/anshulbshah/HaLP)]\n- 3Mformer: Multi-order Multi-mode Transformer for Skeletal Action Recognition [[paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_3Mformer_Multi-Order_Multi-Mode_Transformer_for_Skeletal_Action_Recognition_CVPR_2023_paper.pdf)] [🔥]\n- Unified Pose Sequence Modeling [[paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Foo_Unified_Pose_Sequence_Modeling_CVPR_2023_paper.pdf)]\n- Unified Keypoint-based Action Recognition Framework via Structured Keypoint Pooling [[paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Hachiuma_Unified_Keypoint-Based_Action_Recognition_Framework_via_Structured_Keypoint_Pooling_CVPR_2023_paper.pdf)]\n- Prompt-Guided Zero-Shot Anomaly Action Recognition using Pretrained Deep Skeleton Features [[paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Sato_Prompt-Guided_Zero-Shot_Anomaly_Action_Recognition_Using_Pretrained_Deep_Skeleton_Features_CVPR_2023_paper.pdf)]\n\n**ICCV**\n- Hierarchically Decomposed Graph Convolutional Networks for Skeleton-Based Action Recognition [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Lee_Hierarchically_Decomposed_Graph_Convolutional_Networks_for_Skeleton-Based_Action_Recognition_ICCV_2023_paper.pdf)] [[code](https://github.com/Jho-Yonsei/HD-GCN)] [🔥] [⭐]\n- Generative Action Description Prompts for Skeleton-based Action Recognition [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Xiang_Generative_Action_Description_Prompts_for_Skeleton-based_Action_Recognition_ICCV_2023_paper.pdf)] [[code](https://github.com/MartinXM/GAP)] [⭐]\n- Masked Motion Predictors are Strong 3D Action Representation Learners [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Mao_Masked_Motion_Predictors_are_Strong_3D_Action_Representation_Learners_ICCV_2023_paper.pdf)] [[code](https://github.com/maoyunyao/MAMP)]\n- MotionBERT: A Unified Perspective on Learning Human Motion Representations [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Zhu_MotionBERT_A_Unified_Perspective_on_Learning_Human_Motion_Representations_ICCV_2023_paper.pdf)] [[code](https://github.com/Walter0807/MotionBERT)] [🔥] [⭐]\n- Modeling the Relative Visual Tempo for Self-supervised Skeleton-based Action Recognition [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Zhu_Modeling_the_Relative_Visual_Tempo_for_Self-supervised_Skeleton-based_Action_Recognition_ICCV_2023_paper.pdf)] [[code](https://github.com/Zhuysheng/RVTCLR)]\n- Hard No-Box Adversarial Attack on Skeleton-Based Human Action Recognition with Skeleton-Motion-Informed Gradient [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Lu_Hard_No-Box_Adversarial_Attack_on_Skeleton-Based_Human_Action_Recognition_with_ICCV_2023_paper.pdf)] [[code](https://github.com/luyg45/HardNoBoxAttack)]\n- LAC - Latent Action Composition for Skeleton-based Action Segmentation [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Yang_LAC_-_Latent_Action_Composition_for_Skeleton-based_Action_Segmentation_ICCV_2023_paper.pdf)] [[code](https://github.com/walker1126/Latent_Action_Composition)]\n- Leveraging Spatio-Temporal Dependency for Skeleton-Based Action Recognition [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Lee_Leveraging_Spatio-Temporal_Dependency_for_Skeleton-Based_Action_Recognition_ICCV_2023_paper.pdf)]\n- SkeletonMAE: Graph-based Masked Autoencoder for Skeleton Sequence Pre-training [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Yan_SkeletonMAE_Graph-based_Masked_Autoencoder_for_Skeleton_Sequence_Pre-training_ICCV_2023_paper.pdf)]\n- Parallel Attention Interaction Network for Few-Shot Skeleton-based Action Recognition [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Liu_Parallel_Attention_Interaction_Network_for_Few-Shot_Skeleton-Based_Action_Recognition_ICCV_2023_paper.pdf)]\n- FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Guo_FSAR_Federated_Skeleton-based_Action_Recognition_with_Adaptive_Topology_Structure_and_ICCV_2023_paper.pdf)]\n- SkeleTR: Towards Skeleton-based Action Recognition in the Wild [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Duan_SkeleTR_Towards_Skeleton-based_Action_Recognition_in_the_Wild_ICCV_2023_paper.pdf)]\n- Cross-Modal Learning with 3D Deformable Attention for Action Recognition [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Kim_Cross-Modal_Learning_with_3D_Deformable_Attention_for_Action_Recognition_ICCV_2023_paper.pdf)]\n\n**ICML**\n- Ske2Grid: Skeleton-to-Grid Representation Learning for Action Recognition [[paper](http://proceedings.mlr.press/v202/cai23c/cai23c.pdf)] [[code](https://github.com/OSVAI/Ske2Grid)]\n\n**ICLR**\n- Graph Contrastive Learning for Skeleton-based Action Recognition [[paper](https://arxiv.org/pdf/2301.10900.pdf)] [[code](https://github.com/OliverHxh/SkeletonGCL)] [⭐]\n- Hyperbolic Self-paced Learning for Self-supervised Skeleton-based Action Representations [[paper](https://arxiv.org/pdf/2303.06242.pdf)] [[code](https://github.com/paolomandica/HYSP)]\n\n**AAAI**\n- Hierarchical Consistent Contrastive Learning for Skeleton-Based Action Recognition with Growing Augmentations [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/25451)] [[code](https://github.com/JHang2020/HiCLR)]\n- Self-supervised Action Representation Learning from Partial Spatio-Temporal Skeleton Sequences [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/25495)] [[code](https://github.com/YujieOuO/PSTL)]\n- Frame-Level Label Refinement for Skeleton-Based Weakly-Supervised Action Recognition [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/25439)] [[code](https://github.com/line/Skeleton-Temporal-Action-Localization)]\n- Hierarchical Contrast for Unsupervised Skeleton-based Action Representation Learning [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/25127)] [[code](https://github.com/HuiGuanLab/HiCo)]\n- Anonymization for Skeleton Action Recognition [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/26754)] [[code](https://github.com/ml-postech/Skeleton-anonymization)]\n- Defending Black-box Skeleton-based Human Activity Classifiers [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/25352)] [[code](https://github.com/realcrane/Defending-Black-box-Skeleton-based-Human-Activity-Classifiers)]\n- Novel Motion Patterns Matter for Practical Skeleton-based Action Recognition [[paper](https://humanperception.github.io/documents/AAAI2023.pdf)]\n- Self-Supervised Learning for Multilevel Skeleton-Based Forgery Detection via Temporal-Causal Consistency of Actions [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/25163)]\n\n**ACM MM**\n- Skeleton MixFormer: Multivariate Topology Representation for Skeleton-based Action Recognition [[paper](https://dl.acm.org/doi/abs/10.1145/3581783.3611900)] [[code](https://github.com/ElricXin/Skeleton-MixFormer)]\n- Prompted Contrast with Masked Motion Modeling: Towards Versatile 3D Action Representation Learning [[paper](https://dl.acm.org/doi/abs/10.1145/3581783.3611774)] [[code](https://github.com/JHang2020/PCM3)]\n- Unified Multi-modal Unsupervised Representation Learning for Skeleton-based Action Understanding [[paper](https://dl.acm.org/doi/abs/10.1145/3581783.3612449)] [[code](https://github.com/HuiGuanLab/UmURL)]\n- Zero-shot Skeleton-based Action Recognition via Mutual Information Estimation and Maximization [[paper](https://dl.acm.org/doi/abs/10.1145/3581783.3611888)] [[code](https://github.com/YujieOuO/SMIE)]\n- Self-Relational Graph Convolution Network for Skeleton-Based Action Recognition [[paper](https://dl.acm.org/doi/abs/10.1145/3581783.3612280)]\n- Skeletal Spatial-Temporal Semantics Guided Homogeneous-Heterogeneous Multimodal Network for Action Recognition [[paper](https://dl.acm.org/doi/abs/10.1145/3581783.3612560)]\n- Occluded Skeleton-Based Human Action Recognition with Dual Inhibition Training [[paper](https://dl.acm.org/doi/abs/10.1145/3581783.3612170)]\n\n**IJCAI**\n- Part Aware Contrastive Learning for Self-Supervised Action Recognition [[paper](https://www.ijcai.org/proceedings/2023/0095.pdf)] [[code](https://github.com/GitHubOfHyl97/SkeAttnCLR)]\n- Action Recognition with Multi-stream Motion Modeling and Mutual Information Maximization [[paper](https://www.ijcai.org/proceedings/2023/0184.pdf)]\n\n**ICCVW**\n- A Lightweight Skeleton-Based 3D-CNN for Real-Time Fall Detection and Action Recognition [[paper](https://openaccess.thecvf.com/content/ICCV2023W/JRDB/papers/Noor_A_Lightweight_Skeleton-Based_3D-CNN_for_Real-Time_Fall_Detection_and_Action_ICCVW_2023_paper.pdf)]\n\n**BMVC**\n- STEP CATFormer: Spatial-Temporal Effective Body-Part Cross Attention Transformer for Skeleton-based Action Recognition [[paper](https://arxiv.org/pdf/2312.03288.pdf)] [[code](https://github.com/maclong01/STEP-CATFormer)]\n\n**WACV**\n- Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition [[paper](https://openaccess.thecvf.com/content/WACV2023/papers/Zhu_Adaptive_Local-Component-Aware_Graph_Convolutional_Network_for_One-Shot_Skeleton-Based_Action_Recognition_WACV_2023_paper.pdf)]\n- STAR-Transformer: A Spatio-Temporal Cross Attention Transformer for Human Action Recognition [[paper](https://openaccess.thecvf.com/content/WACV2023/html/Ahn_STAR-Transformer_A_Spatio-Temporal_Cross_Attention_Transformer_for_Human_Action_Recognition_WACV_2023_paper.html)]\n\n**ICIP**\n- Temporal-Channel Topology Enhanced Network for Skeleton-Based Action Recognition [[paper](https://arxiv.org/ftp/arxiv/papers/2302/2302.12967.pdf)]\n- Part Aware Graph Convolution Network with Temporal Enhancement for Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10222714)]\n- Skeleton Action Recognition Based on Spatio-Temporal Features [[paper](https://ieeexplore.ieee.org/abstract/document/10223086)]\n\n**ICME**\n- DD-GCN: Directed Diffusion Graph Convolutional Network for Skeleton-based Human Action Recognition [[paper](https://arxiv.org/pdf/2308.12501.pdf)]\n- Dynamic Spatial-temporal Hypergraph Convolutional Network for Skeleton-based Action Recognition [[paper](https://arxiv.org/abs/2302.08689)]\n\n**WACVW**\n- Challenges in Video-Based Infant Action Recognition: A Critical Examination of the State of the Art [[paper](https://openaccess.thecvf.com/content/WACV2024W/CV4Smalls/papers/Hatamimajoumerd_Challenges_in_Video-Based_Infant_Action_Recognition_A_Critical_Examination_of_WACVW_2024_paper.pdf)]\n\n**ICMEW**\n- SkeletonMAE: Spatial-Temporal Masked Autoencoders for Self-supervised Skeleton Action Recognition [[paper](https://arxiv.org/pdf/2209.02399.pdf)]\n\n**ICASSP**\n- Body Prior Guided Graph Convolutional Neural Network for Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10096690)] [[code](https://github.com/519542630/BPG-GCN)]\n\n**ICIG**\n- Multi-semantic fusion model for generalized zero-shot skeleton-based action recognition [[paper](https://link.springer.com/chapter/10.1007/978-3-031-46305-1_6)] [[code](https://github.com/EHZ9NIWI7/MSF-GZSSAR)]\n\n**IROS**\n- Interactive Spatiotemporal Token Attention Network for Skeleton-based General Interactive Action Recognition [[paper](https://arxiv.org/pdf/2307.07469.pdf)] [[code](https://github.com/Necolizer/ISTA-Net)]\n\n**TPAMI**\n- Self-Supervised 3D Action Representation Learning with Skeleton Cloud Colorization [[paper](https://arxiv.org/pdf/2304.08799.pdf)]\n\n**TIP**\n- DMMG: Dual Min-Max Games for Self-Supervised Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10348505/)]\n\n**TMM**\n- Temporal Decoupling Graph Convolutional Network for Skeleton-based Gesture Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10113233)] [[code](https://github.com/liujf69/TD-GCN-Gesture)] [🔥] [⭐]\n- Delving Deep into One-Shot Skeleton-based Action Recognition with Diverse Occlusions [[paper](https://ieeexplore.ieee.org/abstract/document/10011561)] [[code](https://github.com/KPeng9510/Trans4SOAR)]\n- Skeleton-based Action Recognition through Contrasting Two-Stream Spatial-Temporal Networks [[paper](https://arxiv.org/pdf/2301.11495.pdf)]\n- Learning Representations by Contrastive Spatio-temporal Clustering for Skeleton-based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10227565)]\n- Skeleton-Based Gesture Recognition With Learnable Paths and Signature Features [[paper](https://ieeexplore.ieee.org/abstract/document/10261439)]\n- Skeleton-Based Action Recognition with Select-Assemble-Normalize Graph Convolutional Networks [[paper](https://ieeexplore.ieee.org/abstract/document/10265127)]\n- Joints-Centered Spatial-Temporal Features Fused Skeleton Convolution Network for Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10287687)]\n\n**TCSVT**\n- Motion Complement and Temporal Multifocusing for Skeleton-based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10015806)] [[code](https://github.com/cong-wu/MCMT-Net)]\n- TranSkeleton: Hierarchical Spatial-Temporal Transformer for Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10029908)]\n\n**TNNLS**\n- Spatiotemporal Decouple-and-Squeeze Contrastive Learning for Semi-Supervised Skeleton-based Action Recognition [[paper](https://arxiv.org/pdf/2302.02316.pdf)]\n- Learning Heterogeneous Spatial–Temporal Context for Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10081331)]\n- Self-Adaptive Graph With Nonlocal Attention Network for Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/10250900)]\n\n**PR**\n- Continual spatio-temporal graph convolutional networks [[paper](https://www.sciencedirect.com/science/article/pii/S0031320323002285)] [[code](https://github.com/LukasHedegaard/continual-skeletons)]\n- Relation-mining self-attention network for skeleton-based human action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0031320323001553)] [[code](https://github.com/GedamuA/RSA-Net)]\n- SpatioTemporal focus for skeleton-based action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0031320322007105)]\n- Multi-grained clip focus for skeleton-based action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0031320323008853)]\n\n**Neurocomputing**\n- SPAR: An efficient self-attention network using Switching Partition Strategy for skeleton-based action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S092523122301038X)] [[code](https://github.com/Goldfish0106/SPAR-Network)]\n- Focalized Contrastive View-invariant Learning for Self-supervised Skeleton-based Action Recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0925231223003211)]\n- Spatio-temporal segments attention for skeleton-based action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0925231222013716)]\n- STDM-transformer: Space-time dual multi-scale transformer network for skeleton-based action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0925231223010263)]\n\n**arXiv papers**\n- TSGCNeXt: Dynamic-Static Multi-Graph Convolution for Efficient Skeleton-Based Action Recognition with Long-term Learning Potential [[paper](https://arxiv.org/abs/2304.11631)] [[code](https://github.com/vvhj/TSGCNeXt)]\n- Balanced Representation Learning for Long-tailed Skeleton-based Action Recognition [[paper](https://arxiv.org/abs/2308.14024)] [[code](https://github.com/firework8/BRL)]\n- Cross-Model Cross-Stream Learning for Self-Supervised Human Action Recognition [[paper](https://arxiv.org/abs/2307.07791)] [[code](https://github.com/Levigty/ACL)]\n- Exploring Self-supervised Skeleton-based Action Recognition in Occluded Environments [[paper](https://arxiv.org/abs/2309.12029)] [[code](https://github.com/cyfml/OPSTL)]\n- Pyramid Self-attention Polymerization Learning for Semi-supervised Skeleton-based Action Recognition [[paper](https://arxiv.org/abs/2302.02327)] [[code](https://github.com/1xbq1/PSP-Learning)]\n- Skeleton-based Human Action Recognition via Convolutional Neural Networks (CNN) [[paper](https://arxiv.org/abs/2301.13360)]\n- Cross-view Action Recognition via Contrastive View-invariant Representation [[paper](https://arxiv.org/abs/2305.01733)]\n- Spatial-temporal Transformer-guided Diffusion based Data Augmentation for Efficient Skeleton-based Action Recognition [[paper](https://arxiv.org/abs/2302.13434)]\n- Modiff: Action-Conditioned 3D Motion Generation with Denoising Diffusion Probabilistic Models [[paper](https://arxiv.org/abs/2301.03949)]\n- Skeleton-based action analysis for ADHD diagnosis [[paper](https://arxiv.org/abs/2304.09751)]\n- Fine-grained Action Analysis: A Multi-modality and Multi-task Dataset of Figure Skating [[paper](https://arxiv.org/abs/2307.02730)]\n\n\n### 2022\n\n**CVPR**\n- InfoGCN: Representation Learning for Human Skeleton-based Action Recognition [[paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Chi_InfoGCN_Representation_Learning_for_Human_Skeleton-Based_Action_Recognition_CVPR_2022_paper.pdf)] [[code](https://github.com/stnoah1/infogcn)] [🔥] [⭐]\n- Revisiting Skeleton-based Action Recognition [[paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Duan_Revisiting_Skeleton-Based_Action_Recognition_CVPR_2022_paper.pdf)] [[code](https://github.com/kennymckormick/pyskl)] [🔥] [⭐]\n\n**ECCV**\n- CMD: Self-supervised 3D Action Representation Learning with Cross-modal Mutual Distillation [[paper](https://arxiv.org/pdf/2208.12448.pdf)] [[code](https://github.com/maoyunyao/CMD)]\n- Hierarchically Self-Supervised Transformer for Human Skeleton Representation Learning [[paper](https://arxiv.org/pdf/2207.09644.pdf)] [[code](https://github.com/yuxiaochen1103/Hi-TRS)]\n- Collaborating Domain-shared and Target-specific Feature Clustering for Cross-domain 3D Action Recognition [[paper](https://arxiv.org/pdf/2207.09767.pdf)] [[code](https://github.com/canbaoburen/CoDT)]\n- Global-local Motion Transformer for Unsupervised Skeleton-based Action Learning [[paper](https://arxiv.org/pdf/2207.06101.pdf)] [[code](https://github.com/Boeun-Kim/GL-Transformer)]\n- IGFormer: Interaction Graph Transformer for Skeleton-Based Human Interaction Recognition [[paper](https://arxiv.org/pdf/2207.12100.pdf)]\n- Contrastive Positive Mining for Unsupervised 3D Action Representation Learning [[paper](https://arxiv.org/pdf/2208.03497.pdf)]\n- Learning Spatial-Preserved Skeleton Representations for Few-Shot Action Recognition [[paper](https://openreview.net/pdf?id=qIlLNOJsKxJ)]\n- Uncertainty-DTW for Time Series and Sequences [[paper](https://arxiv.org/pdf/2211.00005.pdf)]\n\n**AAAI**\n- Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/19957)] [[code](https://github.com/Levigty/AimCLR)] [🔥]\n- Topology-aware Convolutional Neural Network for Efficient Skeleton-based Action Recognition [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/20191)] [[code](https://github.com/hikvision-research/skelact)] [🔥] [⭐]\n- Towards To-a-T Spatio-Temporal Focus for Skeleton-Based Action Recognition [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/19998)]\n\n**ACM MM**\n- PYSKL: Towards Good Practices for Skeleton Action Recognition [[paper](https://arxiv.org/pdf/2205.09443.pdf)] [[code](https://github.com/kennymckormick/pyskl)] [🔥] [⭐]\n- Shifting Perspective to See Difference: A Novel Multi-View Method for Skeleton based Action Recognition [[paper](https://arxiv.org/pdf/2209.02986.pdf)] [[code](https://github.com/ideal-idea/SAP)]\n- Skeleton-based Action Recognition via Adaptive Cross-Form Learning [[paper](https://arxiv.org/pdf/2206.15085.pdf)] [[code](https://github.com/stoa-xh91/ACFL)]\n- Self-Supervised Representation Learning for Skeleton-Based Group Activity Recognition [[paper](https://dl.acm.org/doi/abs/10.1145/3503161.3547822)] [[code](https://github.com/xiaochehe/SSL_Skeleton_GAR)]\n- Global-Local Cross-View Fisher Discrimination for View-Invariant Action Recognition [[paper](https://dl.acm.org/doi/abs/10.1145/3503161.3548280)]\n\n**CVPRW**\n- Bootstrapped Representation Learning for Skeleton-Based Action Recognition [[paper](https://openaccess.thecvf.com/content/CVPR2022W/L3D-IVU/papers/Moliner_Bootstrapped_Representation_Learning_for_Skeleton-Based_Action_Recognition_CVPRW_2022_paper.pdf)]\n\n**ECCVW**\n- Mitigating Representation Bias in Action Recognition: Algorithms and Benchmarks [[paper](https://arxiv.org/pdf/2209.09393.pdf)] [[code](https://github.com/kennymckormick/ARAS-Dataset)]\n- PSUMNet: Unified Modality Part Streams are All You Need for Efficient Pose-based Action Recognition [[paper](https://arxiv.org/pdf/2208.05775.pdf)] [[code](https://github.com/skelemoa/psumnet)]\n- Strengthening Skeletal Action Recognizers via Leveraging Temporal Patterns [[paper](https://arxiv.org/pdf/2205.14405.pdf)]\n\n**ACCV**\n- Focal and Global Spatial-Temporal Transformer for Skeleton-based Action Recognition [[paper](https://openaccess.thecvf.com/content/ACCV2022/papers/Gao_Focal_and_Global_Spatial-Temporal_Transformer_for_Skeleton-based_Action_Recognition_ACCV_2022_paper.pdf)] \n- Temporal-Viewpoint Transportation Plan for Skeletal Few-shot Action Recognition [[paper](https://openaccess.thecvf.com/content/ACCV2022/papers/Wang_Temporal-Viewpoint_Transportation_Plan_for_Skeletal_Few-shot_Action_Recognition_ACCV_2022_paper.pdf)]\n\n**WACV**\n- Skeleton-DML: Deep Metric Learning for Skeleton-Based One-Shot Action Recognition [[paper](https://openaccess.thecvf.com/content/WACV2022/papers/Memmesheimer_Skeleton-DML_Deep_Metric_Learning_for_Skeleton-Based_One-Shot_Action_Recognition_WACV_2022_paper.pdf)] [[code](https://github.com/raphaelmemmesheimer/skeleton-dml)]\n- Generative Adversarial Graph Convolutional Networks for Human Action Synthesis [[paper](https://openaccess.thecvf.com/content/WACV2022/papers/Degardin_Generative_Adversarial_Graph_Convolutional_Networks_for_Human_Action_Synthesis_WACV_2022_paper.pdf)] [[code](https://github.com/DegardinBruno/Kinetic-GAN)]\n\n**ICPR**\n- Skeletal Human Action Recognition using Hybrid Attention based Graph Convolutional Network [[paper](https://arxiv.org/pdf/2207.05493.pdf)]\n\n**TPAMI**\n- Constructing Stronger and Faster Baselines for Skeleton-based Action Recognition [[paper](https://arxiv.org/pdf/2106.15125.pdf)] [[code](https://gitee.com/yfsong0709/EfficientGCNv1)] [🔥]\n- Motif-GCNs With Local and Non-Local Temporal Blocks for Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9763364)] [[code](https://github.com/wenyh1616/SAMotif-GCN)]\n- Multi-Granularity Anchor-Contrastive Representation Learning for Semi-Supervised Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9954217)] [[code](https://github.com/1xbq1/MAC-Learning)]\n\n**IJCV**\n- Action2video: Generating Videos of Human 3D Actions [[paper](https://link.springer.com/article/10.1007/s11263-021-01550-z)]\n\n**TIP**\n- Contrast-reconstruction Representation Learning for Self-supervised Skeleton-based Action Recognition [[paper](https://arxiv.org/pdf/2111.11051.pdf)] [[code](https://github.com/Picasso-Wang/CRRL)]\n- Multilevel Spatial–Temporal Excited Graph Network for Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9997556)] [[code](https://github.com/Zhuysheng/ML-STGNet)]\n- SMAM: Self and Mutual Adaptive Matching for Skeleton-Based Few-Shot Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9975251)]\n- X-Invariant Contrastive Augmentation and Representation Learning for Semi-Supervised Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9782720)]\n\n**TMM**\n- Skeleton-Based Mutually Assisted Interacted Object Localization and Human Action Recognition [[paper](https://arxiv.org/pdf/2110.14994.pdf)]\n- Joint-bone Fusion Graph Convolutional Network for Semi-supervised Skeleton Action Recognition [[paper](https://arxiv.org/ftp/arxiv/papers/2202/2202.04075.pdf)]\n\n**TCSVT**\n- Two-person Graph Convolutional Network for Skeleton-based Human Interaction Recognition [[paper](https://arxiv.org/pdf/2208.06174.pdf)] [[code](https://github.com/mgiant/2P-GCN)]\n- Zoom Transformer for Skeleton-Based Group Activity Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9845486)] [[code](https://github.com/Kebii/Zoom-Transformer)]\n- Motion Guided Attention Learning for Self-Supervised 3D Human Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9841515)]\n- Motion-Driven Spatial and Temporal Adaptive High-Resolution Graph Convolutional Networks for Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9931755)]\n- View-Normalized and Subject-Independent Skeleton Generation for Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9940286)]\n\n**TNNLS**\n- Fusing Higher-Order Features in Graph Neural Networks for Skeleton-Based Action Recognition [[paper](https://arxiv.org/pdf/2105.01563.pdf)] [[code](https://github.com/ZhenyueQin/Angular-Skeleton-Encoding)]\n\n**Neurocomputing**\n- Forward-reverse adaptive graph convolutional networks for skeleton-based action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0925231221018920)] [[code](https://github.com/Nanasaki-Ai/FR-AGCN)]\n- AFE-CNN: 3D Skeleton-based Action Recognition with Action Feature Enhancement [[paper](https://arxiv.org/pdf/2208.03444.pdf)]\n- Hierarchical graph attention network with pseudo-metapath for skeleton-based action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0925231222007421)]\n- Skeleton-based similar action recognition through integrating the salient image feature into a center-connected graph convolutional network [[paper](https://www.sciencedirect.com/science/article/pii/S0925231222009560)]\n- PB-GCN: Progressive binary graph convolutional networks for skeleton-based action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0925231222008049)]\n\n**arXiv papers**\n- Hypergraph Transformer for Skeleton-based Action Recognition [[paper](https://arxiv.org/abs/2211.09590)] [[code](https://github.com/ZhouYuxuanYX/Hypergraph-Transformer-for-Skeleton-based-Action-Recognition)] [⭐]\n- DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognition [[paper](https://arxiv.org/abs/2210.05895)] [[code](https://github.com/kennymckormick/pyskl)]\n- Spatio-Temporal Tuples Transformer for Skeleton-Based Action Recognition [[paper](https://arxiv.org/abs/2201.02849)] [[code](https://github.com/heleiqiu/STTFormer)]\n- Contrastive Learning from Spatio-Temporal Mixed Skeleton Sequences for Self-Supervised Skeleton-Based Action Recognition [[paper](https://arxiv.org/abs/2207.03065)] [[code](https://github.com/czhaneva/SkeleMixCLR)]\n- HAA4D: Few-Shot Human Atomic Action Recognition via 3D Spatio-Temporal Skeletal Alignment [[paper](https://arxiv.org/abs/2202.07308)] [[code](https://github.com/Morris88826/HAA4D)]\n- Skeleton-based Action Recognition Via Temporal-Channel Aggregation [[paper](https://arxiv.org/abs/2205.15936)]\n- A New Spatial Adjacency Matrix of Skeleton Data Based on Self-loop and Adaptive Weights [[paper](https://arxiv.org/abs/2206.14344)]\n\n\n### 2021\n\n**CVPR**\n- 3D Human Action Representation Learning via Cross-View Consistency Pursuit [[paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Li_3D_Human_Action_Representation_Learning_via_Cross-View_Consistency_Pursuit_CVPR_2021_paper.pdf)] [[code](https://github.com/LinguoLi/CrosSCLR)] [🔥]\n- BASAR:Black-box Attack on Skeletal Action Recognition [[paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Diao_BASARBlack-Box_Attack_on_Skeletal_Action_Recognition_CVPR_2021_paper.pdf)] [[code](https://github.com/realcrane/BASAR-Black-box-Attack-on-Skeletal-Action-Recognition)]\n- Understanding the Robustness of Skeleton-based Action Recognition under Adversarial Attack [[paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Understanding_the_Robustness_of_Skeleton-Based_Action_Recognition_Under_Adversarial_Attack_CVPR_2021_paper.pdf)] [[code](https://github.com/realcrane/Understanding-the-Robustness-of-Skeleton-based-Action-Recognition-under-Adversarial-Attack)]\n\n**ICCV**\n- Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition [[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Chen_Channel-Wise_Topology_Refinement_Graph_Convolution_for_Skeleton-Based_Action_Recognition_ICCV_2021_paper.pdf)] [[code](https://github.com/Uason-Chen/CTR-GCN)] [🔥] [⭐]\n- AdaSGN: Adapting Joint Number and Model Size for Efficient Skeleton-Based Action Recognition [[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Shi_AdaSGN_Adapting_Joint_Number_and_Model_Size_for_Efficient_Skeleton-Based_ICCV_2021_paper.pdf)] [[code](https://github.com/lshiwjx/AdaSGN)]\n- Skeleton Cloud Colorization for Unsupervised 3D Action Representation Learning [[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Yang_Skeleton_Cloud_Colorization_for_Unsupervised_3D_Action_Representation_Learning_ICCV_2021_paper.pdf)]\n- Self-supervised 3D Skeleton Action Representation Learning with Motion Consistency and Continuity [[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Su_Self-Supervised_3D_Skeleton_Action_Representation_Learning_With_Motion_Consistency_and_ICCV_2021_paper.pdf)]\n- Geometric Deep Neural Network Using Rigid and Non-Rigid Transformations for Human Action Recognition [[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Friji_Geometric_Deep_Neural_Network_Using_Rigid_and_Non-Rigid_Transformations_for_ICCV_2021_paper.pdf)]\n- GeomNet: A Neural Network Based on Riemannian Geometries of SPD Matrix Space and Cholesky Space for 3D Skeleton-Based Interaction Recognition [[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Nguyen_GeomNet_A_Neural_Network_Based_on_Riemannian_Geometries_of_SPD_ICCV_2021_paper.pdf)]\n\n**NeurIPS** \n- Unsupervised Motion Representation Learning with Capsule Autoencoders [[paper](https://proceedings.neurips.cc/paper/2021/file/19ca14e7ea6328a42e0eb13d585e4c22-Paper.pdf)] [[code](https://github.com/ZiweiXU/CapsuleMotion)]\n\n**AAAI**\n- Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/16197)] [[code](https://github.com/czhaneva/MST-GCN)] [🔥]\n- Spatio-Temporal Difference Descriptor for Skeleton-Based Action Recognition [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/16210)]\n\n**ACM MM**\n- Learning Multi-Granular Spatio-Temporal Graph Network for Skeleton-based Action Recognition [[paper](https://arxiv.org/pdf/2108.04536.pdf)] [[code](https://github.com/tailin1009/DualHead-Network)]\n- STST: Spatial-Temporal Specialized Transformer for Skeleton-based Action Recognition [[paper](https://dl.acm.org/doi/abs/10.1145/3474085.3475473)] [[code](https://github.com/HanzoZY/STST)]\n- Skeleton-Contrastive 3D Action Representation Learning [[paper](https://arxiv.org/pdf/2108.03656.pdf)] [[code](https://github.com/fmthoker/skeleton-contrast)]\n- Modeling the Uncertainty for Self-supervised 3D Skeleton Action Representation Learning [[paper](https://dl.acm.org/doi/abs/10.1145/3474085.3475248)]\n\n**CVPRW**\n- One-shot action recognition in challenging therapy scenarios [[paper](https://openaccess.thecvf.com/content/CVPR2021W/LLID/papers/Sabater_One-Shot_Action_Recognition_in_Challenging_Therapy_Scenarios_CVPRW_2021_paper.pdf)] [[code](https://github.com/AlbertoSabater/Skeleton-based-One-shot-Action-Recognition)]\n\n**BMVC**\n- UNIK: A Unified Framework for Real-world Skeleton-based Action Recognition [[paper](https://arxiv.org/pdf/2107.08580.pdf)] [[code](https://github.com/YangDi666/UNIK)]\n- Unsupervised Human Action Recognition with Skeletal Graph Laplacian and Self-Supervised Viewpoints Invariance [[paper](https://arxiv.org/pdf/2204.10312.pdf)] [[code](https://github.com/IIT-PAVIS/UHAR_Skeletal_Laplacian)]\n- LSTA-Net: Long short-term Spatio-Temporal Aggregation Network for Skeleton-based Action Recognition [[paper](https://arxiv.org/abs/2111.00823)]\n\n**WACV**\n- JOLO-GCN: Mining Joint-Centered Light-Weight Information for Skeleton-Based Action Recognition [[paper](https://openaccess.thecvf.com/content/WACV2021/papers/Cai_JOLO-GCN_Mining_Joint-Centered_Light-Weight_Information_for_Skeleton-Based_Action_Recognition_WACV_2021_paper.pdf)]\n\n**ICPR**\n- Learning Connectivity with Graph Convolutional Networks for Skeleton-based Action Recognition [[paper](https://arxiv.org/pdf/2112.03328.pdf)]\n\n**ICPRW**\n- Spatial Temporal Transformer Network for Skeleton-Based Action Recognition [[paper](https://arxiv.org/pdf/2012.06399.pdf)] [[code](https://github.com/Chiaraplizz/ST-TR)] [🔥] [⭐]\n\n**ICIP**\n- Syntactically Guided Generative Embeddings for Zero-Shot Skeleton Action Recognition [[paper](https://arxiv.org/pdf/2101.11530.pdf)] [[code](https://github.com/skelemoa/synse-zsl)]\n\n**ICME**\n- Graph Convolutional Hourglass Networks for Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9428355)]\n\n**ICRA**\n- Pose Refinement Graph Convolutional Network for Skeleton-basedAction Recognition [[paper](https://arxiv.org/pdf/2010.07367.pdf)] [[code](https://github.com/sj-li/PR-GCN)]\n\n**TPAMI**\n- Symbiotic Graph Neural Networks for 3D Skeleton-Based Human Action Recognition and Motion Prediction [[paper](https://arxiv.org/pdf/1910.02212.pdf)] [🔥]\n- Tensor Representations for Action Recognition [[paper](https://arxiv.org/pdf/2012.14371.pdf)]\n\n**IJCV**\n- Quo Vadis, Skeleton Action Recognition? [[paper](https://arxiv.org/pdf/2007.02072.pdf)] [[code](https://skeleton.iiit.ac.in)]\n\n**TIP**\n- Extremely Lightweight Skeleton-Based Action Recognition with ShiftGCN++ [[paper](https://ieeexplore.ieee.org/abstract/document/9515708)] [[code](https://github.com/kchengiva/Shift-GCN-plus)]\n- Structural Knowledge Distillation for Efficient Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9351789)] [[code](https://github.com/xiaochehe/SKD)]\n- Feedback Graph Convolutional Network for Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9626596)]\n- Hypergraph Neural Network for Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9329123)]\n\n**TIFS**\n- REGINA - Reasoning Graph Convolutional Networks in Human Action Recognition [[paper](https://arxiv.org/pdf/2105.06711.pdf)]\n\n**TMM**\n- Prototypical Contrast and Reverse Prediction: Unsupervised Skeleton Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9623511)] [[code](https://github.com/LZU-SIAT/PCRP)]\n- Interaction Relational Network for Mutual Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9319533)] [[code](https://github.com/mauriciolp/inter-rel-net)]\n- LAGA-Net: Local-and-Global Attention Network for Skeleton Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9447926)]\n- A Multi-Stream Graph Convolutional Networks-Hidden Conditional Random Field Model for Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9000721)]\n- Multi-Localized Sensitive Autoencoder-Attention-LSTM For Skeleton-based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9392333)]\n- Dear-Net: Learning Diversities for Skeleton-Based Early Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9667321)]\n- Efficient Spatio-Temporal Contrastive Learning for Skeleton-Based 3-D Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9612062)]\n- GA-Net: A Guidance Aware Network for Skeleton-Based Early Activity Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9661424)]\n\n**TCSVT**\n- Fuzzy Integral-Based CNN Classifier Fusion for 3D Skeleton Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9177170)] [[code](https://github.com/theavicaster/fuzzy-integral-cnn-fusion-3d-har)]\n- A Central Difference Graph Convolutional Operator for Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9597501)] [[code](https://github.com/iesymiao/CD-GCN)]\n- Multi-Stream Interaction Networks for Human Action Recognition [[paper](https://ieeexplore.ieee.org/document/9492107)]\n- A Cross View Learning Approach for Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9496611)]\n- Symmetrical Enhanced Fusion Network for Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9319717)]\n- Graph2Net: Perceptually-enriched graph learning for skeleton-based action recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9446181)]\n\n**TNNLS**\n- Memory Attention Networks for Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9378801)] [[code](https://github.com/memory-attention-networks/MANs)] [🔥]\n\n**PR**\n- Tripool: Graph triplet pooling for 3D skeleton-based action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0031320321001084)]\n- Action recognition using kinematics posture feature on 3D skeleton joint locations [[paper](https://www.sciencedirect.com/science/article/pii/S0167865521000751)]\n- Scene image and human skeleton-based dual-stream human action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0167865521001902)]\n- Dyadic relational graph convolutional networks for skeleton-based human interaction recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0031320321001072)]\n- Arbitrary-view human action recognition via novel-view action generation [[paper](https://www.sciencedirect.com/science/article/pii/S0031320321002302)]\n\n**Neurocomputing**\n- Rethinking the ST-GCNs for 3D skeleton-based human action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0925231221007153)]\n- Attention adjacency matrix based graph convolutional networks for skeleton-based action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0925231221002101)]\n- Skeleton-based action recognition using sparse spatio-temporal GCN with edge effective resistance [[paper](https://www.sciencedirect.com/science/article/pii/S0925231220317094)]\n- Integrating vertex and edge features with Graph Convolutional Networks for skeleton-based action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0925231221013928)]\n- Adaptive multi-view graph convolutional networks for skeleton-based action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0925231220317690)]\n- Knowledge embedded GCN for skeleton-based two-person interaction recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0925231220317732)]\n- Normal graph: Spatial temporal graph convolutional networks based prediction network for skeleton based video anomaly detection [[paper](https://www.sciencedirect.com/science/article/pii/S0925231220317720)]\n\n**arXiv papers**\n- STAR: Sparse Transformer-based Action Recognition [[paper](https://arxiv.org/abs/2107.07089)] [[code](https://github.com/imj2185/STAR)]\n- Self-attention based anchor proposal for skeleton-based action recognition [[paper](https://arxiv.org/abs/2112.09413)] [[code](https://github.com/ideal-idea/SAP)]\n- Multi-Scale Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition [[paper](https://arxiv.org/abs/2111.03993)]\n- 3D Skeleton-based Few-shot Action Recognition with JEANIE is not so Na¨ıve [[paper](https://arxiv.org/abs/2112.12668)]\n\n\n### 2020\n\n**CVPR**\n- Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Liu_Disentangling_and_Unifying_Graph_Convolutions_for_Skeleton-Based_Action_Recognition_CVPR_2020_paper.pdf)] [[code](https://github.com/kenziyuliu/ms-g3d)] [🔥] [⭐]\n- Skeleton-Based Action Recognition with Shift Graph Convolutional Network [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Cheng_Skeleton-Based_Action_Recognition_With_Shift_Graph_Convolutional_Network_CVPR_2020_paper.pdf)] [[code](https://github.com/kchengiva/Shift-GCN)] [🔥] [⭐]\n- Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_Semantics-Guided_Neural_Networks_for_Efficient_Skeleton-Based_Human_Action_Recognition_CVPR_2020_paper.pdf)] [[code](https://github.com/microsoft/SGN)] [🔥] [⭐]\n- PREDICT \u0026 CLUSTER: Unsupervised Skeleton Based Action Recognition [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Su_PREDICT__CLUSTER_Unsupervised_Skeleton_Based_Action_Recognition_CVPR_2020_paper.pdf)] [[code](https://github.com/shlizee/Predict-Cluster)] [⭐]\n- Dynamic Multiscale Graph Neural Networks for 3D Skeleton Based Human Motion Prediction [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Dynamic_Multiscale_Graph_Neural_Networks_for_3D_Skeleton_Based_Human_CVPR_2020_paper.pdf)] [[code](https://github.com/limaosen0/DMGNN)] [🔥] [⭐]\n- Context Aware Graph Convolution for Skeleton-Based Action Recognition [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_Context_Aware_Graph_Convolution_for_Skeleton-Based_Action_Recognition_CVPR_2020_paper.pdf)]\n\n**ECCV**\n- Decoupling GCN with DropGraph Module for Skeleton-Based Action Recognition [[paper](https://link.springer.com/chapter/10.1007/978-3-030-58586-0_32)] [[code](https://github.com/kchengiva/DecoupleGCN-DropGraph)] [🔥]\n- Unsupervised 3D Human Pose Representation with Viewpoint and Pose Disentanglement [[paper](https://link.springer.com/chapter/10.1007/978-3-030-58529-7_7)] [[code](https://github.com/NIEQiang001/unsupervised-human-pose)]\n- DDGCN: A Dynamic Directed Graph Convolutional Network for Action Recognition [[paper](https://link.springer.com/chapter/10.1007/978-3-030-58565-5_45)]\n- Adversarial Self-supervised Learning for Semi-supervised 3D Action Recognition [[paper](https://link.springer.com/chapter/10.1007/978-3-030-58571-6_3)]\n\n**AAAI**\n- Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/5652)] [[code](https://github.com/xiaoiker/GCN-NAS)] [🔥] [⭐]\n- Part-Level Graph Convolutional Network for Skeleton-Based Action Recognition [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/6759)]\n- Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/6911)]\n\n**ACM MM**\n- Stronger, Faster and More Explainable: A Graph Convolutional Baseline for Skeleton-based Action Recognition [[paper](https://arxiv.org/pdf/2010.09978.pdf)] [[code](https://gitee.com/yfsong0709/ResGCNv1)] [🔥]\n- Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action Recognition [[paper](https://arxiv.org/pdf/2007.14690.pdf)] [[code](https://github.com/hikvision-research/skelact)] [⭐]\n- Spatio-Temporal Inception Graph Convolutional Networks for Skeleton-Based Action Recognition [[paper](https://arxiv.org/pdf/2011.13322.pdf)] [[code](https://github.com/yellowtownhz/STIGCN)]\n- MS2L: Multi-Task Self-Supervised Learning for Skeleton Based Action Recognition [[paper](https://arxiv.org/pdf/2010.05599.pdf)] [[code](https://github.com/LanglandsLin/MS2L)]\n- Action2Motion: Conditioned Generation of 3D Human Motions [[paper](https://arxiv.org/pdf/2007.15240.pdf)] [[code](https://github.com/EricGuo5513/action-to-motion)] [⭐]\n- Group-Skeleton-Based Human Action Recognition in Complex Events [[paper](https://arxiv.org/ftp/arxiv/papers/2011/2011.13273.pdf)]\n- Mix Dimension in Poincaré Geometry for 3D Skeleton-based Action Recognition [[paper](https://arxiv.org/pdf/2007.15678.pdf)]\n\n**NIPSW**\n- Contrastive Self-Supervised Learning for Skeleton Action Recognition [[paper](http://proceedings.mlr.press/v148/gao21a/gao21a.pdf)]\n\n**ACCV**\n- Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action-Gesture Recognition [[paper](https://openaccess.thecvf.com/content/ACCV2020/papers/Shi_Decoupled_Spatial-Temporal_Attention_Network_for_Skeleton-Based_Action-Gesture_Recognition_ACCV_2020_paper.pdf)]\n\n**TPAMI**\n- Learning Multi-View Interactional Skeleton Graph for Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9234715)] [[code](https://github.com/niais/mv-ignet)]\n- Multi-Task Deep Learning for Real-Time 3D Human Pose Estimation and Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9007695)] [[code](https://github.com/dluvizon/deephar)] [⭐]\n\n**TIP**\n- Skeleton-Based Action Recognition with Multi-Stream Adaptive Graph Convolutional Networks [[paper](https://arxiv.org/pdf/1912.06971.pdf)] [[code](https://github.com/lshiwjx/2s-AGCN)] [🔥] [⭐]\n\n**TMM**\n- Hierarchical Soft Quantization for Skeleton-Based Human Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/9076822)]\n- Deep Manifold-to-Manifold Transforming Network for Skeleton-Based Action Recognition [[paper](https://ieeexplore.ieee.org/abstract/document/8960323)]\n\n**TCSVT**\n- Richly Activated Graph Convolutional Network for Robust Skeleton-based Action Recognition [[paper](https://arxiv.org/pdf/2008.03791.pdf)] [[code](https://github.com/wqk666999/RA-GCNv2)]\n\n**TNNLS**\n- Adversarial Attack on Skeleton-Based Human Action Recognition [[paper](https://arxiv.org/pdf/1909.06500.pdf)]\n\n**TOMM**\n- A Benchmark Dataset and Comparison Study for Multi-modal Human Action Analytics [[paper](http://39.96.165.147/Pub%20Files/2020/ssj_tomm20.pdf)]\n\n**PR**\n- Skeleton-based action recognition with hierarchical spatial reasoning and temporal stack learning network [[paper](https://www.sciencedirect.com/science/article/pii/S0031320320303149)]\n\n**Neurocomputing**\n- Exploring a rich spatial–temporal dependent relational model for skeleton-based action recognition by bidirectional LSTM-CNN [[paper](https://www.sciencedirect.com/science/article/pii/S0925231220311760)]\n- HDS-SP: A novel descriptor for skeleton-based human action recognition [[paper](https://www.sciencedirect.com/science/article/pii/S0925231219316509)]\n\n\n### 2019\n\n**CVPR**\n- Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition [[paper](https://openaccess.thecvf.com/content_CVPR_2019/papers/Shi_Two-Stream_Adaptive_Graph_Convolutional_Networks_for_Skeleton-Based_Action_Recognition_CVPR_2019_paper.pdf)] [[code](https://github.com/lshiwjx/2s-AGCN)] [🔥] [⭐]\n- Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition [[paper](https://openaccess.thecvf.com/content_CVPR_2019/papers/Li_Actional-Structural_Graph_Convolutional_Networks_for_Skeleton-Based_Action_Recognition_CVPR_2019_paper.pdf)] [[code](https://github.com/limaosen0/AS-GCN)] [🔥] [⭐]\n- Skeleton-Based Action Recognition with Directed Graph Neural Networks [[paper](https://openaccess.thecvf.com/content_CVPR_2019/papers/Shi_Skeleton-Based_Action_Recognition_With_Directed_Graph_Neural_Networks_CVPR_2019_paper.pdf)] [[code](https://github.com/kenziyuliu/DGNN-PyTorch)] [🔥] [⭐]\n- Bayesian Hierarchical Dynamic Model for Human Action Recognition [[paper](https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_Bayesian_Hierarchical_Dynamic_Model_for_Human_Action_Recognition_CVPR_2019_paper.pdf)] [[code](https://github.com/rort1989/HDM)]\n- An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition [[paper](https://openaccess.thecvf.com/content_CVPR_2019/papers/Si_An_Attention_Enhanced_Graph_Convolutional_LSTM_Network_for_Skeleton-Based_Action_CVPR_2019_paper.pdf)] [🔥]\n\n**ICCV**\n- Bayesian Graph Convolution LSTM for Skeleton Based Action Recognition [[paper](https://openaccess.thecvf.com/content_ICCV_2019/papers/Zhao_Bayesian_Graph_Convolution_LSTM_for_Skeleton_Based","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffirework8%2FAwesome-Skeleton-based-Action-Recognition","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffirework8%2FAwesome-Skeleton-based-Action-Recognition","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffirework8%2FAwesome-Skeleton-based-Action-Recognition/lists"}