用LLM 提高SD的生成效果,已经是一种常用的做法,主要是利用LLM将图片的描述转成SD的prompt(SD的一些短词的prompt生成效果会好于直白的自然文本)。本文的做法更有意义,直接利用LLM的in context learning 和位置理解能力,生成带有推理约束prompt的图片,如以下例子 具体做法为下图的两个阶段 第一个阶段是通过LLM生成Layout...
LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models O网页链接ChatPaper综述:在使用扩散模型生成文本到图像时,仍然存在一些困难,尤其是对于需要空间或常识推理的提示。为了解决这个问题,研究者提出了一种新的方法,通过使用预训练的大规模语言模型(LLM)...
Text-conditioned diffusion models have emerged as a promising tool for neural video generation. However, current models still struggle with intricate spatiotemporal prompts and often generate restricted or incorrect motion. To address these limitations, we introduce LLM-grounded Video Diffusion (LVD). In...
如上图所示,Groma 作者对比了不同的 Grounded MLLMs 的实现形式,前两者分别是(a) 使用 LLM 输出的数值来定位;(b)LLM 输出 token 使用外部工具来定位;(c)Groma 的形式:localized visual tokenization,即先用一个通用的目标检测模型,把图像中的目标检测出来,然后再用 LLM 输出对应的目标的索引从而实现定位。 Grom...
LLM-grounded Video Diffusion Models (LVD)是基于LLM的视频扩散模型,其官方实现是为了支持LVD论文。该模型利用语言-图像联合预训练模型(LLM)来实现视频内容的扩散和生成。通过结合自然语言描述和视觉信息,LVD能够实现对视频内容的理解和创作,具有更好的视觉动态生成能力。该模型在ICLR 2024会议上有相关研究成果,并提供了...
To address these limitations, we introduce LLM-grounded Video Diffusion (LVD). Instead of directly generating videos from the text inputs, LVD first leverages a large language model (LLM) to generate dynamic scene layouts based on the text inputs and subsequently uses the generated layouts to ...
Video Large Language Models (Video-LLMs) have demonstrated remarkable capabilities in coarse-grained video understanding, however, they struggle with fine-grained temporal grounding. In this paper, we introduce Grounded-VideoLLM, a novel Video-LLM adept at perceiving and reasoning over specific video ...
Video Large Language Models (Video-LLMs) have demonstrated remarkable capabilities in coarse-grained video understanding, however, they struggle with fine-grained temporal grounding. In this paper, we introduce Grounded-VideoLLM, a novel Video-LLM adept at perceiving and reasoning over specific video ...
PitVQA: Image-Grounded Text Embedding LLM forVisual Question Answering inPituitary Surgerydoi:10.1007/978-3-031-72089-5_46Visual Question Answering (VQA) within the surgical domain, utilizing Large Language Models (LLMs), offers a distinct opportunity to improve intra-operative decision-making and ...
The result is a robust, knowledge-grounded reasoning pipeline that improves both the efficiency and accuracy of LLM-based problem solving. Features Multi-Step LLM Reasoning: Agents call the LLM multiple times to generate a solution, perform validation, assess with a numeric score and confidence, an...