1 citations · 1 across the 10 of their papers we have counts for
12 papers
MQAdapter: Multi-Modal Quantum Adapter for Coarse-to-Fine VLM Fine-tuning
Yumiao Zhao, Bo Jiang, Min Lu +2
Large-scale Vision-Language Models have demonstrated impressive transfer learning capabilities across a wide range of tasks. For few-shot classification, we observe that VLMs exhib…
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…
Robust and Generalizable GNN Fine-Tuning via Uncertainty-aware Adapter Learning
Bo Jiang, Weijun Zhao, Beibei Wang +2
Recently, fine-tuning large-scale pre-trained GNNs has yielded remarkable attention in adapting pre-trained GNN models for downstream graph learning tasks. One representative fine-…
Fine-Grained VLM Fine-tuning via Latent Hierarchical Adapter Learning
Yumiao Zhao, Bo Jiang, Yuhe Ding +3
Adapter-based approaches have garnered attention for fine-tuning pre-trained Vision-Language Models (VLMs) on few-shot classification tasks. These methods strive to develop a light…
Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter
Bo Jiang, Xueyang Ze, Beibei Wang +3
Textual adapter-based tuning methods have shown significant potential in transferring knowledge from pre-trained Vision-Language Models (VLMs) to downstream tasks. Existing works g…
UGG-ReID: Uncertainty-Guided Graph Model for Multi-Modal Object Re-Identification
Xixi Wan, Aihua Zheng, Bo Jiang +3
Multi-modal object Re-IDentification (ReID) has gained considerable attention with the goal of retrieving specific targets across cameras using heterogeneous visual data sources. A…
Reliable Multi-Modal Object Re-Identification via Modality-Aware Graph Reasoning
Xixi Wan, Aihua Zheng, Zi Wang +3
Multi-modal data provides abundant and diverse object information, crucial for effective modal interactions in Re-Identification (ReID) tasks. However, existing approaches often ov…
HeGraphAdapter: Tuning Multi-Modal Vision-Language Models with Heterogeneous Graph Adapter
Yumiao Zhao, Bo Jiang, Xiao Wang +2
Adapter-based tuning methods have shown significant potential in transferring knowledge from pre-trained Vision-Language Models to the downstream tasks. However, after reviewing ex…