64 citations · 70 across the 6 of their papers we have counts for
6 papers
PRISM: Self-Pruning Intrinsic Selection Method for Training-Free Multimodal Data Selection
Jinhe Bi, Aniri, Zengjie Jin +11
Visual instruction tuning adapts pre-trained Multimodal Large Language Models (MLLMs) to follow human instructions for real-world applications. However, the rapid growth of these d…
FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models
Haokun Chen, Hang Li, Yao Zhang +7
One-Shot Federated Learning (OSFL), a special decentralized machine learning paradigm, has recently gained significant attention. OSFL requires only a single round of client data o…
Can Multimodal Large Language Models Truly Perform Multimodal In-Context Learning?
Shuo Chen, Zhen Han, Bailan He +6
Large Language Models (LLMs) with in-context learning (ICL) ability can quickly adapt to a specific context given a few demonstrations (demos). Recently, Multimodal Large Language…
GenTKG: Generative Forecasting on Temporal Knowledge Graph with Large Language Models
Ruotong Liao, Xu Jia, Yangzhe Li +2
The rapid advancements in large language models (LLMs) have ignited interest in the temporal knowledge graph (tKG) domain, where conventional embedding-based and rule-based methods…
A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models
Jindong Gu, Zhen Han, Shuo Chen +7
Prompt engineering is a technique that involves augmenting a large pre-trained model with task-specific hints, known as prompts, to adapt the model to new tasks. Prompts can be cre…
An Unsupervised Joint System for Text Generation from Knowledge Graphs and Semantic Parsing
Martin Schmitt, Sahand Sharifzadeh, Volker Tresp +1
Knowledge graphs (KGs) can vary greatly from one domain to another. Therefore supervised approaches to both graph-to-text generation and text-to-graph knowledge extraction (semanti…