DDGCN: A Dynamic Directed Graph Convolutional Network for Action Recognition
We propose a Dynamic Directed Graph Convolutional Network (DDGCN) to model spatial and temporal features of human actions from their skeletal representations. The DDGCN consists of three new feature modeling modules: (1) Dynamic Convolutional Sampling (DCS), (2) Dynamic Convolutional Weight (DCW) assignment, and (3) Directed Graph Spatial-Temporal (DGST) feature extraction. Comprehensive experiments show that the DDGCN outperforms existing state-of-the-art action recognition approaches in various testing datasets. Our source code and model will be released at http://www.ece.lsu.edu/xinli/ActionModeling/index.html .
Code (1)
Tasks
Action RecognitionSimilar Papers 제목 키워드 기반
DDGCN: Dual Dynamic Graph Convolutional Networks for Rumor Detection on Social Media
Detecting rumors on social media has become particular important due to the rapid dissemination and adverse impacts on our lives. Though a set of rumor detection models have exploited the message propagation structural o…
Knowledge GraphsA Fractional Graph Laplacian Approach to Oversmoothing
Graph neural networks (GNNs) have shown state-of-the-art performances in various applications. However, GNNs often struggle to capture long-range dependencies in graphs due to oversmoothing. In this paper, we generalize …
DD-GCN: Directed Diffusion Graph Convolutional Network for Skeleton-based Human Action Recognition
Graph Convolutional Networks (GCNs) have been widely used in skeleton-based human action recognition. In GCN-based methods, the spatio-temporal graph is fundamental for capturing motion patterns. However, existing approa…
Action RecognitionTemporal Action LocalizationDynBERG: Dynamic BERT-based Graph neural network for financial fraud detection
Financial fraud detection is critical for maintaining the integrity of financial systems, particularly in decentralised environments such as cryptocurrency networks. Although Graph Convolutional Networks (GCNs) are widel…
Graph Neural NetworkGraph ClassificationFraud DetectionInformation Extraction from Visually Rich Documents Using Directed Weighted Graph Neural Network
This paper presents a novel approach to information extraction (IE) from visually rich documents (VRD) by employing a directed weighted graph representation to capture relationships among various VRD components. In contr…
Document Layout Analysisdocument understandingGraph Neural NetworkInformation Retrieval+2