Diverse and High-Quality Food Image Generation from Only Food Names

doi:10.1145/3730588

	Diverse and High-Quality Food Image Generation from Only Food Names
	Yu, Dongjian 1; Min, Weiqing 2,3; Jin, Xin 1; Jiang, Qian 1; Jin, Ying 2,3; Jiang, Shuqiang 2,3
	2025-05-01
发表期刊	ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
ISSN	1551-6857
卷号	21 期号:5 页码:22
摘要	Food image generation holds promising application prospects in food design, advertising, and food education. However, the existing methods rely on information such as recipes, ingredients, or food names, which leads to generated food images with less intra-class diversity. When recipes, ingredients, and food names are identical for the same food, the real-world images may vary significantly in appearance. The question of how to simultaneously ensure the quality and diversity of the generated images is a key issue. To this end, we employ pre-trained diffusion model and Transformer to propose a method for generating diverse and high-quality images of both Chinese and Western food, named CW-Food. Different from previous works that utilize an overall food feature to generate new images, CW-Food first decouples the food images to obtain common intra-class features and private instance features. Additionally, we design a Transformer-based feature fusion module to integrate the common and private features, in order to avoid the shortcomings of conventional methods. Moreover, we also utilize a pre-trained diffusion model as our backbone, which is finetuned using LoRA with the fused multi-variate features. Extensive experiments on four datasets demonstrate the advantages of our proposed method, producing diverse and high-quality food images encompassing both Chinese and Western cuisines. To the best of our knowledge, our work is the first attempt to generate Chinese food images using only food names.
关键词	Food image Fine-grained image generation Diffusion model
DOI	10.1145/3730588
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62261060] ; National Natural Science Foundation of China[62125207] ; Beijing Natural Science Foundation[JQ24021] ; Yunnan Fundamental Research Projects[202301AW070007] ; Yunnan Fundamental Research Projects[202301AU070210] ; Yunnan Fundamental Research Projects[202401AT070470] ; Yunnan Province Major Science and Technology Project[202202AD080002] ; Yunnan Province Expert Workstations[202305AF150078] ; Xingdian Talent Project in Yunnan Province ; Yunnan University 4th Graduate Professional Degree Innovation and Practice Project[ZC-24248562]
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS记录号	WOS:001520921500001
出版者	ASSOC COMPUTING MACHINERY
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/42292
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Jin, Xin
作者单位	1.Yunnan Univ, Sch Software, Kunming, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Yu, Dongjian,Min, Weiqing,Jin, Xin,et al. Diverse and High-Quality Food Image Generation from Only Food Names[J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,2025,21(5):22.
APA	Yu, Dongjian,Min, Weiqing,Jin, Xin,Jiang, Qian,Jin, Ying,&Jiang, Shuqiang.(2025).Diverse and High-Quality Food Image Generation from Only Food Names.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,21(5),22.
MLA	Yu, Dongjian,et al."Diverse and High-Quality Food Image Generation from Only Food Names".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 21.5(2025):22.