From the 1 of 12 linked papers with an AI index.
8 papers · 1 filter
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…
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…
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…