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Skeleton Based Action Recognition

Skeleton-based Action Recognition is a computer vision task that involves recognizing human actions from a sequence of 3D skeletal joint data captured from sensors such as Microsoft Kinect, Intel RealSense, and wearable devices. The goal of skeleton-based action recognition is to develop algorithms that can understand and classify human actions from skeleton data, which can be used in various applications such as human-computer interaction, sports analysis, and surveillance.

( Image credit: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition )

Papers

Showing 125 of 419 papers

TitleStatusHype
MotionBERT: A Unified Perspective on Learning Human Motion RepresentationsCode3
Revealing Key Details to See Differences: A Novel Prototypical Perspective for Skeleton-based Action RecognitionCode2
DeGCN: Deformable Graph Convolutional Networks for Skeleton-Based Action RecognitionCode2
SkateFormer: Skeletal-Temporal Transformer for Human Action RecognitionCode2
BlockGCN: Redefine Topology Awareness for Skeleton-Based Action RecognitionCode2
Hulk: A Universal Knowledge Translator for Human-Centric TasksCode2
Zero-shot Skeleton-based Action Recognition with Prototype-guided Feature AlignmentCode1
SkeletonX: Data-Efficient Skeleton-based Action Recognition via Cross-sample Feature AggregationCode1
Siformer: Feature-isolated Transformer for Efficient Skeleton-based Sign Language RecognitionCode1
BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket SportsCode1
DSTSA-GCN: Advancing Skeleton-Based Gesture Recognition with Semantic-Aware Spatio-Temporal Topology ModelingCode1
USDRL: Unified Skeleton-Based Dense Representation Learning with Multi-Grained Feature DecorrelationCode1
TDSM: Triplet Diffusion for Skeleton-Text Matching in Zero-Shot Action RecognitionCode1
Language-Assisted Skeleton Action Understanding for Skeleton-Based Temporal Action SegmentationCode1
CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action RecognitionCode1
SHARP: Segmentation of Hands and Arms by Range using Pseudo-Depth for Enhanced Egocentric 3D Hand Pose Estimation and Action RecognitionCode1
Multi-Modality Co-Learning for Efficient Skeleton-based Action RecognitionCode1
SA-DVAE: Improving Zero-Shot Skeleton-Based Action Recognition by Disentangled Variational AutoencodersCode1
Frequency Guidance Matters: Skeletal Action Recognition by Frequency-Aware Mixed TransformerCode1
Shap-Mix: Shapley Value Guided Mixing for Long-Tailed Skeleton Based Action RecognitionCode1
STARS: Self-supervised Tuning for 3D Action Recognition in Skeleton SequencesCode1
Real-Time Hand Gesture Recognition: Integrating Skeleton-Based Data Fusion and Multi-Stream CNNCode1
Part-aware Unified Representation of Language and Skeleton for Zero-shot Action RecognitionCode1
HDBN: A Novel Hybrid Dual-branch Network for Robust Skeleton-based Action RecognitionCode1
GCN-DevLSTM: Path Development for Skeleton-Based Action RecognitionCode1
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