论文分享:《Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs》 zZZ 2 人赞同了该文章 论文地址:arxiv.org/pdf/2404.0571 这篇论文介绍了Ferret-UI,这是一个由Apple研究团队开发的多模态大型语言模型(MLLM),专门为理解和交互移动用户界面(UI)屏幕而设计。Ferret-UI通过结合先进的视觉和语言处理...
论文地址: https://arxiv.org/pdf/2404.03413.pdf一、文章总结本文介绍了 MiniGPT4-Video,这是一个专为视频理解而设计的多模态大型语言模型(LLM)。MiniGPT4-Video在MiniGPT-v2的基础上进行了显著的创新和改进…
Recent advancements in multimodal large language models (MLLMs) have been noteworthy, yet, these general-domain MLLMs often fall short in their ability to comprehend and interact effectively with user interface (UI) screens. In this paper, we present Ferret-UI, a new MLLM tailored for enhanced...
Paper tables with annotated results for WorldSense: Evaluating Real-world Omnimodal Understanding for Multimodal LLMs
Understanding domain-specific theorems often requires more than just text-based reasoning; effective communication through structured visual explanations is crucial for deeper comprehension. While large language models (LLMs) demonstrate strong performance in text-based theorem reasoning, their ability to ...
It not only enriches the representation of multimodal travel features but also captures the spatiotemporal dependencies between different travel modes, offering a more comprehensive view of multimodal trips. Leveraging the embedding model from LLMs, the textual representation of multimodal travel features ...
This repo contains the evaluation framework for the paper: VisualWebBench: How Far Have Multimodal LLMs Evolved in Web Page Understanding and Grounding? 🌐 Homepage | 🤗 Dataset | 📖 arXiv Update [2024/10/18]: We introduce 🤗 MultiUI, 7.3M general multimodal instructions synthesized from...
Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model ScalingJanus-Pro is an advanced version of the previous work Janus. Specifically, Janus-Pro incorporates (1) an optimized training strategy, (2) expanded training data, and (3) scaling to larger model size. With ...
Publication|Publication Multimodal large language models (MLLMs) have recently achieved impressive general-purpose vision-language capabilities through visual instruction tuning. However, current MLLMs primarily focus on image-level or box-level understanding, falling short in achieving fine-grai...
While multimodal models (LMMs) have advanced significantly for text and image tasks, video-based models remain underdeveloped. Videos are inherently complex, combining spatial and temporal dimensions that demand more from computational resources. Existing methods often adapt im...