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Skeleton Based Action Recognition
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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.
<span style="color:grey; opacity: 0.6">( Image credit: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
SA-DVAE: Improving Zero-Shot Skeleton-Based Action Recognition by Disentangled Variational Autoencoders
Shap-Mix: Shapley Value Guided Mixing for Long-Tailed Skeleton Based Action Recognition
Frequency Guidance Matters: Skeletal Action Recognition by Frequency-Aware Mixed Transformer
STARS: Self-supervised Tuning for 3D Action Recognition in Skeleton Sequences
Do You Act Like You Talk? Exploring Pose-based Driver Action Classification with Speech Recognition Networks
Boosting Adversarial Transferability for Skeleton-based Action Recognition via Exploring the Model Posterior Space
Mask and Compress: Efficient Skeleton-based Action Recognition in Continual Learning
Expressive Keypoints for Skeleton-based Action Recognition via Skeleton Transformation
Real-Time Hand Gesture Recognition: Integrating Skeleton-Based Data Fusion and Multi-Stream CNN
Part-aware Unified Representation of Language and Skeleton for Zero-shot Action Recognition
An Information Compensation Framework for Zero-Shot Skeleton-based Action Recognition
Skeleton-OOD: An End-to-End Skeleton-Based Model for Robust Out-of-Distribution Human Action Detection
Enhancing Action Recognition from Low-Quality Skeleton Data via Part-Level Knowledge Distillation
An Improved Graph Pooling Network for Skeleton-Based Action Recognition
HDBN: A Novel Hybrid Dual-branch Network for Robust Skeleton-based Action Recognition
MK-SGN: A Spiking Graph Convolutional Network with Multimodal Fusion and Knowledge Distillation for Skeleton-based Action Recognition
In My Perspective, In My Hands: Accurate Egocentric 2D Hand Pose and Action Recognition
Multi-Scale Spatial-Temporal Self-Attention Graph Convolutional Networks for Skeleton-based Action Recognition
LLMs are Good Action Recognizers
DeGCN: Deformable Graph Convolutional Networks for Skeleton-Based Action Recognition
GCN-DevLSTM: Path Development for Skeleton-Based Action Recognition
Skeleton-Based Human Action Recognition with Noisy Labels
CrossGLG: LLM Guides One-shot Skeleton-based 3D Action Recognition in a Cross-level Manner
SkateFormer: Skeletal-Temporal Transformer for Human Action Recognition
Taylor Videos for Action Recognition
Wavelet-Decoupling Contrastive Enhancement Network for Fine-Grained Skeleton-Based Action Recognition
AutoGCN -- Towards Generic Human Activity Recognition with Neural Architecture Search
Benchmarking Sensitivity of Continual Graph Learning for Skeleton-Based Action Recognition
Unsupervised Spatial-Temporal Feature Enrichment and Fidelity Preservation Network for Skeleton based Action Recognition
Skeleton2vec: A Self-supervised Learning Framework with Contextualized Target Representations for Skeleton Sequence
Spatial-Temporal Decoupling Contrastive Learning for Skeleton-based Human Action Recognition
Navigating Open Set Scenarios for Skeleton-based Action Recognition
STEP CATFormer: Spatial-Temporal Effective Body-Part Cross Attention Transformer for Skeleton-based Action Recognition
Hulk: A Universal Knowledge Translator for Human-Centric Tasks
VSViG: Real-time Video-based Seizure Detection via Skeleton-based Spatiotemporal ViG
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