Hierarchical bidirected graph convolutions for large-scale 3-D point cloud place recognition
In this article, we present a novel hierarchical bidirected graph convolution network (HiBi-
GCN) for large-scale 3-D point cloud place recognition. Unlike place recognition methods …
GCN) for large-scale 3-D point cloud place recognition. Unlike place recognition methods …
Retrieval-augmented generation with graphs (graphrag)
Retrieval-augmented generation (RAG) is a powerful technique that enhances downstream
task execution by retrieving additional information, such as knowledge, skills, and tools from …
task execution by retrieving additional information, such as knowledge, skills, and tools from …
Capturing word positions does help: A multi-element hypergraph gated attention network for document classification
Y **, W Yin, H Wang, F He - Expert Systems with Applications, 2024 - Elsevier
Over the last few years, graph-based methods have manifested a significant enhancement
in document mining applications such as spam detection, news recommendation, and legal …
in document mining applications such as spam detection, news recommendation, and legal …
Topic-aware hierarchical multi-attention network for text classification
Neural networks, primarily recurrent and convolutional Neural networks, have been proven
successful in text classification. However, convolutional models could be limited when …
successful in text classification. However, convolutional models could be limited when …
Multi-modal long document classification based on Hierarchical Prompt and Multi-modal Transformer
In the realm of long document classification (LDC), previous research has predominantly
focused on modeling unimodal texts, overlooking the potential of multi-modal documents …
focused on modeling unimodal texts, overlooking the potential of multi-modal documents …
Tackling Real-world Complexity: Hierarchical Modeling and Dynamic Prompting for Multimodal Long Document Classification
With the rapid growth of internet content, multimodal long document data has become
increasingly prominent, drawing significant attention from researchers. However, most …
increasingly prominent, drawing significant attention from researchers. However, most …
Hierarchy-Aware Adaptive Graph Neural Network
Graph Neural Networks (GNNs) have gained attention for their ability in capturing node
interactions to generate node representations. However, their performances are frequently …
interactions to generate node representations. However, their performances are frequently …
Hierarchical Multi-Granularity Interaction Graph Convolutional Network for Long Document Classification
With the growing demand for text analytics, long document classification (LDC) has received
extensive attention, and great progress has been made. To reveal the complex structure and …
extensive attention, and great progress has been made. To reveal the complex structure and …
Hierarchical Multi-modal Prompting Transformer for Multi-modal Long Document Classification
In the context of long document classification (LDC), effectively utilizing multi-modal
information encompassing texts and images within these documents has not received …
information encompassing texts and images within these documents has not received …
BIGFormer: A Graph Transformer with Local Structure Awareness for Diagnosis and Pathogenesis Identification of Alzheimer's Disease Using Imaging Genetic Data
Alzheimer's disease (AD) is a highly inheritable neurological disorder, and brain imaging
genetics (BIG) has become a rapidly advancing field for comprehensive understanding its …
genetics (BIG) has become a rapidly advancing field for comprehensive understanding its …