Large Multi-modal Models Can Interpret Features in Large Multi-modal ModelsO网页链接这篇论文探讨了如何理解和解释大规模多模态模型(LMMs)的内部神经表示。研究提出了一种多功能的框架,用于识别和解释LMMs中的语义。首先,研究者使用稀疏自动编码器(SAE)将表示分离成人类可理解的特征。接着,他们构建了一个自动解释...
Monkey: Image Resolution and Text Label Are Important Things for Large Multi-modal Models 树雨 17 人赞同了该文章 本文简要介绍由华中科技大学联合金山的研究人员提出的多模态大模型Monkey,通过低成本扩大分辨率+详细描述,帮助模型炼就洞察图像细节的火眼金睛,刷新多项SOTA,甚至能够完成GPT4V都发愁的密集文本问答...
为了实现更平滑的训练初始化,我们对转移的窗口注意力进行了修改,允许它们从零初始化开始学习,避免在初始阶段对早期特征进行过度转换。 特别是,受17的启发,我们将 MLP 中的常规初始化修改为零初始化,以实现更平滑的训练:x = \text{BA}\hat{x}\tag{1}其中B和A指的是两个线性层的权重。 我们对 $A$ 使...
Incorporating additional modalities to LLMs (Large Language Models) creates LMMs (Large Multimodal Models). Not all multimodal systems are LMMs. For example, text-to-image models like Midjourney, Stable Diffusion, and Dall-E are multimodal but don’t have a language model component. Multimodal ca...
ICLR 2024|June 2023 Despite the promising progress in multi-modal tasks, current large multi-modal models (LMMs) are prone to hallucinating inconsistent descriptions with respect to the associated image and human instructions. This paper addresses this issue by introduc...
MiniCPM-V 2.0提供领先的中英双语多模态能力支持。 该能力通过VisCPM[ICLR'24] 论文中提出的多模态能力的跨语言泛化技术实现。 性能评估 TextVQA, DocVQA, OCRBench, OpenCompass, MME, MMBench, MMMU, MathVista, LLaVA Bench, Object HalBench 上的详细评测结果。
The structural similarities between protein sequences and natural languages have led to parallel advancements in deep learning across both domains.While large language models (LLMs) have achieved much progress in the domain of natural language processing, their potential in protein engineering remains ...
BEIJING, Sept. 19 (Xinhua) -- A geographic sciences multi-modal Large Language Model (LLM), the first of its kind in the world, was unveiled in Beijing on Thursday. It could support the integration of geography and artificial intelligence and help accelerate geographical discoveries. ...
Monkey (LMM): Image Resolution and Text Label Are Important Things for Large Multi-modal Models - flyinter/Monkey
up new possibilities for AI application scenarios, but also enhanced capabilities such as comprehensive codebase analysis, autonomous completion of multi-step complex tasks by intelligent agents, perpetual assistants that retain crucial information, and genuinely unified architecture of multimodal models. ...