SOTAVerified

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 5160 of 419 papers

TitleStatusHype
Fusing Higher-order Features in Graph Neural Networks for Skeleton-based Action RecognitionCode1
Logsig-RNN: a novel network for robust and efficient skeleton-based action recognitionCode1
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action RecognitionCode1
MMNet: A Model-Based Multimodal Network for Human Action Recognition in RGB-D VideosCode1
Frequency Guidance Matters: Skeletal Action Recognition by Frequency-Aware Mixed TransformerCode1
HDBN: A Novel Hybrid Dual-branch Network for Robust Skeleton-based Action RecognitionCode1
A Dense-Sparse Complementary Network for Human Action Recognition based on RGB and Skeleton ModalitiesCode1
GCN-DevLSTM: Path Development for Skeleton-Based Action RecognitionCode1
BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket SportsCode1
HYperbolic Self-Paced Learning for Self-Supervised Skeleton-based Action RepresentationsCode1
Show:102550
← PrevPage 6 of 42Next →

No leaderboard results yet.