In this paper, we design and train a Generative Image-to-text Transformer, GIT, to unify vision-language tasks such as image/video captioning and question answering. While generative models provide a consistent network architecture between pre-training and fine-tuning, exist...
This repo presents some example codes to reproduce some results inGIT: A Generative Image-to-text Transformer for Vision and Language. Installation Installazfuse. The tool is used to automatically download the data. The configuration of AzFuse has already been in this repo. ...
1.论文标题: Generative Image Dynamics 论文链接: 论文作者: 内容简介: 方法论: 应用: 实验与结果: 2.论文标题: Rich Human Feedback for Text-to-Image Generation 论文链接: 论文作者: 内容简介: 1.论文标题: Generative Image Dynamics 论文链接: https://arxiv.org/pdf/2309.07906 论文作者: Zhengqi Li...
概括而言,DALL-E-2 训练了 3 个模型来完成文生图(Text-to-Image): CLIP 模型:负责将文本和视觉图像联系起来 GLIDE 模型:负责从视觉的描述中产生图像 PRIOR 模型:负责把文本描述映射到视觉描述 这里再次强调我们在第一集中,就提及的 Transformer 模型的重要性。
关于文字生成图像（Text-to-Image）方向的论文解读、示例代码等我们还会有其他专题深入讨论。以上就是关于 Transformer 和 Generative AI 的部分介绍。在下一篇文章中，我们将详细讨论关于 Generative AI 另一个重要的进步方向就是：文字生成（Text Generation）方向。分享这个领域的最新进展，以及亚马逊云科技在为支持这些...
Title: Muse: Text-To-Image Generation via Masked Generative Transformers From Google Research. ICML 2023. 我的博客Highlight作者提出了一种在离散token空间下的掩码建模任务上训练的图像生成模型MUSE。和d…
概括而言,DALL-E-2 训练了 3 个模型来完成文生图(Text-to-Image): CLIP 模型:负责将文本和视觉图像联系起来 GLIDE 模型:负责从视觉的描述中产生图像 PRIOR 模型:负责把文本描述映射到视觉描述 这里再次强调我们在第一集中,就提及的 Transformer 模型的重要性。
从第一个Text-to-Image生成模型的演变来看 从第二个Text-to-Text Transformer-based的ChatGPT的提出来看 3.实际行业应用 对行业格局的理解:尽管AIGC需要巨量算力、资金和研发人才、调参人员,Generative AI本质是一个"巨头的生意",目前成立的大量创业公司会被收购或者消失,但行业的生态位和社会化分工依然...
In this paper, we design and train a Generative Image-to-text Transformer, GIT, to unify vision-language tasks such as image/video captioning and question answering. While generative models provide a consistent network architecture between pre-training and fine-tuning, existing work typically ...
We present Muse, a text-to-image Transformer model that achieves state-of-the-art image generation performance while being significantly more efficient than diffusion or autoregressive models. Muse is trained on a masked modeling task in discrete token space: given the text embedding extracted from ...