From the 1 of 11 linked papers with an AI index.
11 papers
MQAdapter: Multi-Modal Quantum Adapter for Coarse-to-Fine VLM Fine-tuning
Yumiao Zhao, Bo Jiang, Min Lu +2
The paper introduces MQAdapter, a parameter‑efficient adapter that uses quantum state encoding to refine visual features with top‑K semantic anchors, enabling coarse‑to‑fine fine‑t…
FEMOT: Multi-Object Tracking using Frame and Event Cameras
Shiao Wang, Xiao Wang, Chao Wang +6
Conventional RGB cameras have been widely used in multi-object tracking due to their ability to capture rich appearance and semantic information. However, their performance is ofte…
Beyond Low-Rank: Low-Rank Sparse Prompting via Spiking Neural Network and Prompt Factorization
Yumiao Zhao, Bo Jiang, Beibei Wang +3
Visual Prompting (VP) has emerged as an efficient paradigm for adapting large-scale pre-trained vision models to downstream tasks by incorporating learnable prompts at the input le…
Unified Graph Prompt Learning via Low-Rank Graph Message Prompting
Beibei Wang, Bo Jiang, Ziyan Zhang +1
Graph Data Prompt (GDP), which introduces specific prompts in graph data for efficiently adapting pre-trained GNNs, has become a mainstream approach to graph fine-tuning learning p…
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…