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Found 15 papers, 11 papers with code
Skeleton-Parted Graph Scattering Networks for 3D Human Motion Prediction
To address the first issue, we propose adaptive graph scattering, which leverages multiple trainable band-pass graph filters to decompose pose features into richer graph spectrum bands.
GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoning
From the aspect of interaction capturing, we propose a trainable multiscale hypergraph to capture both pair-wise and group-wise interactions at multiple group sizes.
Multiscale Spatio-Temporal Graph Neural Networks for 3D Skeleton-Based Motion Prediction
The core of MST-GNN is a multiscale spatio-temporal graph that explicitly models the relations in motions at various spatial and temporal scales.
Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Network
This paper considers predicting future statuses of multiple agents in an online fashion by exploiting dynamic interactions in the system.
Incremental Embedding Learning via Zero-Shot Translation
Different from traditional incremental classification networks, the semantic gap between the embedding spaces of two adjacent tasks is the main challenge for embedding networks under incremental learning setting.
Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation
To handle the decomposition ambiguity in the teacher network, we propose a cycle-consistent architecture promoting a 3D rotation-invariant property to train the teacher network.
Sampling and Recovery of Graph Signals based on Graph Neural Networks
Compared to previous analytical sampling and recovery, the proposed methods are able to flexibly learn a variety of graph signal models from data by leveraging the learning ability of neural networks; compared to previous neural-network-based sampling and recovery, the proposed methods are designed through exploiting specific graph properties and provide interpretability.
Graph Cross Networks with Vertex Infomax Pooling
Based on trainable hierarchical representations of a graph, GXN enables the interchange of intermediate features across scales to promote information flow.
Decoupled Variational Embedding for Signed Directed Networks
Comprehensive results on both link sign prediction and node recommendation task demonstrate the effectiveness of DVE.
Towards Transferable Targeted Attack
Furthermore, we regularize the targeted attack process with metric learning to take adversarial examples away from true label and gain more transferable targeted adversarial examples.
Dynamic Multiscale Graph Neural Networks for 3D Skeleton Based Human Motion Prediction
The core idea of DMGNN is to use a multiscale graph to comprehensively model the internal relations of a human body for motion feature learning.
Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction
The core idea of DMGNN is to use a multiscale graph to comprehensively model the internal relations of a human body for motion feature learning.
Attribute Restoration Framework for Anomaly Detection
We here propose to break this equivalence by erasing selected attributes from the original data and reformulate it as a restoration task, where the normal and the anomalous data are expected to be distinguishable based on restoration errors.
Ranked #21 on
Anomaly Detection
on One-class CIFAR-10
Symbiotic Graph Neural Networks for 3D Skeleton-based Human Action Recognition and Motion Prediction
For the backbone, we propose multi-branch multi-scale graph convolution networks to extract spatial and temporal features.
Ranked #39 on
Skeleton Based Action Recognition
on NTU RGB+D
Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition
We validate AS-GCN in action recognition using two skeleton data sets, NTU-RGB+D and Kinetics.
