前言自从ChatGPT问世以来,人工智能领域经历了一场令人眼花缭乱的变革,特别是在视觉-语言模型(Vision-Language Models, VLMs)的研究和应用上更是如此。VLMs通过结合视觉感知能力和自然语言理解能力,已经在诸如…
《Meta最新Vision-Language Model研究综述(一)——VLMs的分类》在开头部分提到有多种训练VLM的方法。一些方法使用简单的对比训练标准,另一些使用掩码策略来预测缺失的文本或图像块,还有一些模型使用生成范式,如自回归或扩散。此外,还可以利用预训练的视觉或文本主干网络,如Llama或GPT。在这种情况下,构建VLM模型只需学习...
全开源数据集训练vision language model全开源数据集训练视觉语言模型(Vision-Language Models)是一个研究领域,旨在开发能够理解和生成图像和文本的混合模型的算法。这些模型通常被训练来执行各种任务,如图像描述、视觉问答、图像字幕生成、跨模态检索等。 要训练一个视觉语言模型,通常需要以下几个步骤: 1. 数据收集:首先...
Vision language models (VLMs) are multimodal generative AI models capable of reasoning over text, image, and video prompts. What Are Vision Language Models? Vision language models are multimodal AI systems built by combining a large language model (LLM) with a vision encoder, giving the LLM the...
整体的思想非常简单,对于一些任务来说,我们无法使用数学式子写出奖励函数,但是我们可以使用自然语言描述。同时现在 vision-language model 可以同时对文字和图像进行编码,我们认为相似文字对应的图像在隐空间内会是相似的,于是我们利用这个相似来作为奖励。 参考资料...
In 2021, OpenAI introduced its foundation model known as Contrastive Language-Image Pre-training (CLIP), which suggested how LLM innovations might be combined with other processing techniques. Stability AI – in conjunction with researchers from Ludwig Maximilian University of Munich and Runway AI --...
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[CVPR 2024] RegionGPT: Towards Region Understanding Vision Language Model [Paper][Code] [ICLR 2024] LLMs Meet VLMs: Boost Open Vocabulary Object Detection with Fine-grained Descriptors [Paper] [ICLR 2024] Ins-DetCLIP: Aligning Detection Model to Follow Human-Language Instruction [Paper] VLM Kno...
Here, to address this challenge and improve the performance of cardiac imaging models, we developed EchoCLIP, a vision–language foundation model for echocardiography, that learns the relationship between cardiac ultrasound images and the interpretations of expert cardiologists across a wide range of ...
本文受到前人对抗训练方法的启发,将其用到 vision-language model 的预训练中。 该方法的核心部分有如下三点: 对抗的预训练和微调机制; 在映射空间添加干扰; 增强的对抗训练方法; 1. 对抗预训练与微调: Pre-training: 给定预训练数据集,包含 image-text pairs,训练的目标就是在这个大型数据集上学习一些与任务无...