8 citations · 9 across the 5 of their papers we have counts for
5 papers
S2FGL: Spatial Spectral Federated Graph Learning
Zihan Tan, Suyuan Huang, Guancheng Wan +3
Federated Graph Learning (FGL) combines the privacy-preserving capabilities of federated learning (FL) with the strong graph modeling capability of Graph Neural Networks (GNNs). Cu…
Keeping Yourself is Important in Downstream Tuning Multimodal Large Language Model
Wenke Huang, Jian Liang, Xianda Guo +14
Multi-modal Large Language Models (MLLMs) integrate visual and linguistic reasoning to address complex tasks such as image captioning and visual question answering. While MLLMs dem…
Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning
Wenke Huang, Jian Liang, Zekun Shi +6
Multimodal Large Language Model (MLLM) have demonstrated strong generalization capabilities across diverse distributions and tasks, largely due to extensive pre-training datasets.…
All in One Framework for Multimodal Re-identification in the Wild
He Li, Mang Ye, Ming Zhang +1
In Re-identification (ReID), recent advancements yield noteworthy progress in both unimodal and cross-modal retrieval tasks. However, the challenge persists in developing a unified…
Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark
Wenke Huang, Mang Ye, Zekun Shi +4
Federated learning has emerged as a promising paradigm for privacy-preserving collaboration among different parties. Recently, with the popularity of federated learning, an influx…