Visual-language alignment is crucial for enhancing the domain adaptability of industrial anomaly detection models. However, the existing methods overlook the importance of structured image ...

This letter proposes a multiple station-based seismic event classification model using a deep convolution neural network (CNN) and graph convolution network (GCN). To classify various seismic events, ...

To achieve this objective, we introduce a text-graph grounding paradigm that generates prompts designed to preserve the graph’s structural context for language models. This paradigm acts as a bridge, ...

Graph Convolutional Networks (GCN) and their variants utilize learnable weight matrices and nonlinear activation functions to extract features from data. The selection of activation functions and ...

Surprisingly the performances of a star-like and snowflake-like graph data warehouses are very close. Hence a snowflake schema could be used in order to easily consider new sub-dimensions in a graph ...