4 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…
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…
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…
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…