Learning to Generate Semantic Layouts for Higher Text-Image Correspondence in Text-to-Image Synthesis-Reference-Cited by-同舟云学术

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Learning to Generate Semantic Layouts for Higher Text-Image Correspondence in Text-to-Image Synthesis

Published:2023-10-01 Issue: Volume:34 Page:7557-7566
ISSN:
Container-title:2023 IEEE/CVF International Conference on Computer Vision (ICCV)
language:
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Author:

Park Minho¹,Yun Jooyeol¹,Choi Seunghwan¹,Choo Jaegul¹

Affiliation:

1. Korea Advanced Institute of Science and Technology (KAIST),Daejeon,Korea

Funder

National Research Foundation

Electronics and Telecommunications Research Institute

NAV

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10376473/10376477/10376919.pdf?arnumber=10376919

Reference43 articles.

1. Structured denoising diffusion models in discrete state-spaces;Austin;NeurIPS,2021

2. Label-efficient semantic segmentation with diffusion models;Baranchuk

3. FocalClick: Towards Practical Interactive Image Segmentation

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