25 citations · 34 across the 5 of their papers we have counts for
5 papers
Detecting Complex Multi-step Attacks with Explainable Graph Neural Network
Wei Liu, Peng Gao, Haotian Zhang +4
Complex multi-step attacks have caused significant damage to numerous critical infrastructures. To detect such attacks, graph neural network based methods have shown promising resu…
ChartAssisstant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning
Fanqing Meng, Wenqi Shao, Quanfeng Lu +4
Charts play a vital role in data visualization, understanding data patterns, and informed decision-making. However, their unique combination of graphical elements (e.g., bars, line…
BESA: Pruning Large Language Models with Blockwise Parameter-Efficient Sparsity Allocation
Peng Xu, Wenqi Shao, Mengzhao Chen +6
Large language models (LLMs) have demonstrated outstanding performance in various tasks, such as text summarization, text question-answering, and etc. While their performance is im…
Improving Compositional Text-to-image Generation with Large Vision-Language Models
Song Wen, Guian Fang, Renrui Zhang +3
Recent advancements in text-to-image models, particularly diffusion models, have shown significant promise. However, compositional text-to-image models frequently encounter difficu…
ImageBind-LLM: Multi-modality Instruction Tuning
Jiaming Han, Renrui Zhang, Wenqi Shao +14
We present ImageBind-LLM, a multi-modality instruction tuning method of large language models (LLMs) via ImageBind. Existing works mainly focus on language and image instruction tu…