This allows Graph Neural Networks (GNNs) to be applied to broader unstructured domains such as 3D face analysis. GSL can be considered as the dynamic learning of connection weights within a layer of message pas
Third, most of these methods overlook the global properties of a graph, because they are based on random walk-based skip-gram model or graph convolutional network (GCN) [13], both of which are known to be effective for capturing the local graph structure [22]. More precisely, nodes that ...
IntagHand [23] propose an attention-based graph convolutional network (GCN) for two-hand vertex regression from an RGB image. These recent deep learning-based frameworks [23, 32, 39, 42] have shown that (1) an attention mechanism to model non-local int...
& Wang, S. SedSVD: Statement-level software vulnerability detection based on Relational Graph Convolutional Network with subgraph embedding. Inf. Softw. Technol. 158, 107168 (2023). Article Google Scholar Li, L. et al. VulANalyzeR: Explainable binary vulnerability detection with multi-task ...
3.2 Graph Inference Inspired by [52], the graph inference is performed by using the mean field and computing the hidden states with Long Short-Term Memory (LSTM) net- work [22], which is an effective recurrent neural network. Let the semantic graph be G = (S, V, E), where S is ...
3.2 Graph Inference Inspired by [52], the graph inference is performed by using the mean field and computing the hidden states with Long Short-Term Memory (LSTM) net- work [22], which is an effective recurrent neural network. Let the semantic graph be G = (S, V, E), where S is ...
(PAA) module for enhanced pillar features extraction whilesuppressing noises in the point clouds. By integratingmulti-point-channel-pooling, point-wise, channel-wise, and task-aware attentioninto a simple module, the representation capabilities are boosted whilerequiring little additional computing ...
Moreover, the results showed that the proposed model achieves similar or comparable results with simpler deep models when trained on a public tweet dataset such as ACL 2014 dataset while outperforming both simple deep models and state-of-the-art graph convolutional deep mode...
On Eth, GCN-VRNN, based on a Graph Convolutional Neural Network, generates trajectories that significantly drift from the ground-truth ones. On Zara1, all considered models are able to follow correct paths, but AC-VRNN appears more able to predict complex trajectory such as the entrance into...
(we view gray-scale image as one channel but still 3-D tensor, such as for MNIST the dimension is [28], [1]), but for most multi-view data, each view is always a kind of 1-D hand-craft feature of raw data that the fully convolutional network is not suitable for this case; ...