adapter fine-tuning 1cross-modal representation 1few-shot learning 1quantum computing 1vision-language models 1
From the 1 of 3 linked papers with an AI index.
3 papers
cs.CV2026
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…
cs.CV2026
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…
cs.CV2025
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…