12 citations · 47 across the 32 of their papers we have counts for
6 papers · 1 filter
Vision Enhancing LLMs: Empowering Multimodal Knowledge Storage and Sharing in LLMs
Yunxin Li, Zhenyu Liu, Baotian Hu +4
Recent advancements in multimodal large language models (MLLMs) have achieved significant multimodal generation capabilities, akin to GPT-4. These models predominantly map visual i…
A Comprehensive Evaluation of GPT-4V on Knowledge-Intensive Visual Question Answering
Yunxin Li, Longyue Wang, Baotian Hu +5
The emergence of multimodal large models (MLMs) has significantly advanced the field of visual understanding, offering remarkable capabilities in the realm of visual question answe…
Training Multimedia Event Extraction With Generated Images and Captions
Zilin Du, Yunxin Li, Xu Guo +2
Contemporary news reporting increasingly features multimedia content, motivating research on multimedia event extraction. However, the task lacks annotated multimodal training data…
A Multi-Modal Context Reasoning Approach for Conditional Inference on Joint Textual and Visual Clues
Yunxin Li, Baotian Hu, Xinyu Chen +3
Conditional inference on joint textual and visual clues is a multi-modal reasoning task that textual clues provide prior permutation or external knowledge, which are complementary…
A Neural Divide-and-Conquer Reasoning Framework for Image Retrieval from Linguistically Complex Text
Yunxin Li, Baotian Hu, Yuxin Ding +2
Pretrained Vision-Language Models (VLMs) have achieved remarkable performance in image retrieval from text. However, their performance drops drastically when confronted with lingui…
LMEye: An Interactive Perception Network for Large Language Models
Yunxin Li, Baotian Hu, Xinyu Chen +3
Training a Multimodal Large Language Model (MLLM) from scratch, like GPT-4, is resource-intensive. Regarding Large Language Models (LLMs) as the core processor for multimodal infor…