6 papers
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…
Integrated Structural Prompt Learning for Vision-Language Models
Jiahui Wang, Qin Xu, Bo Jiang +1
Prompt learning methods have significantly extended the transferability of pre-trained Vision-Language Models (VLMs) like CLIP for various downstream tasks. These methods adopt han…
Dynamic Rank Adaptation for Vision-Language Models
Jiahui Wang, Qin Xu, Bo Jiang +1
Pre-trained large vision-language models (VLMs) like CLIP demonstrate impressive generalization ability. Existing prompt-based and adapter-based works have made significant progres…
Harmonizing and Merging Source Models for CLIP-based Domain Generalization
Yuhe Ding, Jian Liang, Bo Jiang +3
CLIP-based domain generalization aims to improve model generalization to unseen domains by leveraging the powerful zero-shot classification capabilities of CLIP and multiple source…
Unifying Graph Contrastive Learning via Graph Message Augmentation
Ziyan Zhang, Bo Jiang, Jin Tang +1
Graph contrastive learning is usually performed by first conducting Graph Data Augmentation (GDA) and then employing a contrastive learning pipeline to train GNNs. As we know that…
Robust Transductive Few-shot Learning via Joint Message Passing and Prototype-based Soft-label Propagation
Jiahui Wang, Qin Xu, Bo Jiang +1
Few-shot learning (FSL) aims to develop a learning model with the ability to generalize to new classes using a few support samples. For transductive FSL tasks, prototype learning a…