Grounded Text-to-Image Synthesis with Attention Refocusing O网页链接ChatPaper综述:本文讨论在大规模文本-图像对数据集上训练的扩展性扩散模型驱动下,文本-图像合成方法已经展示出了强大的结果,但当文本提示中涉及多个对象、属性和空间组合时,这些模型仍然无法精确地遵循文本提示。作者在本文中发现这个问题的潜在原因...
论文解读 Open-Set Grounded Text-to-Image Generation CVPR 2023:GLIGEN: Open-Set Grounded Text-to-Image Generation 1. 论文信息 论文题目:GLIGEN: Open-Set Grounded Text-to-Image Generation 作者:Yuheng Li, Haotian Liu, Qingyang Wu, Fangzhou Mu, Jianwei Yang, Jianfeng Gao, Chunyuan Li, Yong Jae...
GLIGEN: Open-Set Grounded Text-to-Image Generation Yuheng Li1§, Haotian Liu1§, Qingyang Wu2, Fangzhou Mu1, Jianwei Yang3, Jianfeng Gao3, Chunyuan Li3¶, Yong Jae Lee1¶ 1University of Wisconsin-Madison 2Columbia University 3Microsoft https://gligen.github.io/ (a) Caption: ...
It is important to note that our model GLIGEN is designed for open-world grounded text-to-image generation with caption and various condition inputs (e.g. bounding box). However, we also recognize the importance of responsible AI considerations and the need to clearly communicate the capabilitie...
However, the status quo is to use text input alone, which can impede controllability. In this work, we propose Gligen, Grounded-Language-to-Image Generation, a novel approach that builds upon and extends the functionality of existing pre-trained text-to-image diffusion models by enabling them ...
Official implementation of "IFAdapter: Instance Feature Control for Grounded Text-to-Image Generation". - WUyinwei-hah/IFAdapter
box[1])), (int(box[2]), int(box[3])), (255, 0, 255))# cv2.putText(image, text1,...
GLIP的主要贡献如下：将phrase grounding和目标检测任务统一，将image和text prompt同时输入到目标检测网络...
1) it allows GLIP to learn from both detection and grounding data to improve both tasks and bootstrap a good grounding model; 2) GLIP can leverage massive image-text pairs by generating grounding boxes in a self-training fashion, making the learned representation semantic-rich. In our experim...
Transparent medical image AI via an image–text foundation model grounded in medical literaturedoi:10.1038/s41591-024-02887-xBuilding trustworthy and transparent image-based medical artificial intelligence (AI) systems requires the ability to interrogate data and models at all stages of the development ...