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: ...
GLIGEN: Open-Set Grounded Text-to-Image Generation (CVPR 2023) Yuheng Li,Haotian Liu,Qingyang Wu,Fangzhou Mu,Jianwei Yang,Jianfeng Gao,Chunyuan Li*,Yong Jae Lee*(*Co-senior authors) [Project Page] [Paper] [Demo] [YouTube Video]
Large-scale text-to-image diffusion models have made amazing advances. 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...
Grounding DINO with GLIGEN for Controllable Image Editing OpenSeeD: A Simple and Strong Openset Segmentation Model SEEM: Segment Everything Everywhere All at Once X-GPT: Conversational Visual Agent supported by X-Decoder GLIGEN: Open-Set Grounded Text-to-Image Generation LLaVA: Large Language and...
Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection" - Yufan-Bao/GroundingDINO
[ECCV 2024] Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection" - IDEA-Research/GroundingDINO
Includes: a text backbone, an image backbone, a feature enhancer, a language-guided query selection, and a cross-modality decoder. ♥️Acknowledgement Our model is related toDINOandGLIP. Thanks for their great work! We also thank great previous work including DETR, Deformable DETR, SMCA, ...
Perhaps most importantly, we re-iterate that OpenVAE is unique in that it provides a grounded basis to conduct continual learning in the presence of unknown data. However, as evidenced from the quantitative open-set recognition results, the inclusion of unknown data instances into continual learning...
Includes: a text backbone, an image backbone, a feature enhancer, a language-guided query selection, and a cross-modality decoder. ♥️Acknowledgement Our model is related toDINOandGLIP. Thanks for their great work! We also thank great previous work including DETR, Deformable DETR, SMCA, ...