2 citations · 3 across the 5 of their papers we have counts for
6 papers
When Prompting Meets Spiking: Graph Sparse Prompting via Spiking Graph Prompt Learning
Bo Jiang, Weijun Zhao, Beibei Wang +1
Graph Prompt Feature (GPF) learning has been widely used in adapting pre-trained GNN model on the downstream task. GPFs first introduce some prompt atoms and then learns the optima…
Robust Graph Fine-Tuning with Adversarial Graph Prompting
Ziyan Zhang, Bo Jiang, Jin Tang
Parameter-Efficient Fine-Tuning (PEFT) method has emerged as a dominant paradigm for adapting pre-trained GNN models to downstream tasks. However, existing PEFT methods usually exh…
Reliable and Compact Graph Fine-tuning via GraphSparse Prompting
Bo Jiang, Hao Wu, Beibei Wang +2
Recently, graph prompt learning has garnered increasing attention in adapting pre-trained GNN models for downstream graph learning tasks. However, existing works generally conduct…
Graph Edge Representation via Tensor Product Graph Convolutional Representation
Bo Jiang, Sheng Ge, Ziyan Zhang +3
Graph Convolutional Networks (GCNs) have been widely studied. The core of GCNs is the definition of convolution operators on graphs. However, existing Graph Convolution (GC) operat…
A Unified Graph Selective Prompt Learning for Graph Neural Networks
Bo Jiang, Hao Wu, Ziyan Zhang +2
In recent years, graph prompt learning/tuning has garnered increasing attention in adapting pre-trained models for graph representation learning. As a kind of universal graph promp…
Context-Semantic Quality Awareness Network for Fine-Grained Visual Categorization
Qin Xu, Sitong Li, Jiahui Wang +2
Exploring and mining subtle yet distinctive features between sub-categories with similar appearances is crucial for fine-grained visual categorization (FGVC). However, less effort…