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Found 5 papers, 1 papers with code
DGNN: Decoupled Graph Neural Networks with Structural Consistency between Attribute and Graph Embedding Representations
To obtain a more comprehensive embedding representation of nodes, a novel GNNs framework, dubbed Decoupled Graph Neural Networks (DGNN), is introduced.
Grassmannian Graph-attentional Landmark Selection for Domain Adaptation
GGLS presents a landmark selection scheme using attention-induced neighbors of the graphical structure of samples and performs distribution adaptation and knowledge adaptation over Grassmann manifold.
GAN for Vision, KG for Relation: a Two-stage Deep Network for Zero-shot Action Recognition
In the sampling stage, we utilize a generative adversarial networks (GAN) trained by action features and word vectors of seen classes to synthesize the action features of unseen classes, which can balance the training sample data of seen classes and unseen classes.
Real-time Human Action Recognition Using Locally Aggregated Kinematic-Guided Skeletonlet and Supervised Hashing-by-Analysis Model
To tackle all these problems, we propose a real-time 3D action recognition framework by integrating the locally aggregated kinematic-guided skeletonlet (LAKS) with a supervised hashing-by-analysis (SHA) model.
Feature Fusion Use Unsupervised Prior Knowledge to Let Small Object Represent
Fusing low level and high level features is a widely used strategy to provide details that might be missing during convolution and pooling.
