ChefFusion:整合食谱和食物图像生成的多模态基础模型

Peiyu Li, Xiaobao Huang, Yijun Tian, Nitesh V. Chawla
{"title":"ChefFusion:整合食谱和食物图像生成的多模态基础模型","authors":"Peiyu Li, Xiaobao Huang, Yijun Tian, Nitesh V. Chawla","doi":"arxiv-2409.12010","DOIUrl":null,"url":null,"abstract":"Significant work has been conducted in the domain of food computing, yet\nthese studies typically focus on single tasks such as t2t (instruction\ngeneration from food titles and ingredients), i2t (recipe generation from food\nimages), or t2i (food image generation from recipes). None of these approaches\nintegrate all modalities simultaneously. To address this gap, we introduce a\nnovel food computing foundation model that achieves true multimodality,\nencompassing tasks such as t2t, t2i, i2t, it2t, and t2ti. By leveraging large\nlanguage models (LLMs) and pre-trained image encoder and decoder models, our\nmodel can perform a diverse array of food computing-related tasks, including\nfood understanding, food recognition, recipe generation, and food image\ngeneration. Compared to previous models, our foundation model demonstrates a\nsignificantly broader range of capabilities and exhibits superior performance,\nparticularly in food image generation and recipe generation tasks. We\nopen-sourced ChefFusion at GitHub.","PeriodicalId":501130,"journal":{"name":"arXiv - CS - Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ChefFusion: Multimodal Foundation Model Integrating Recipe and Food Image Generation\",\"authors\":\"Peiyu Li, Xiaobao Huang, Yijun Tian, Nitesh V. Chawla\",\"doi\":\"arxiv-2409.12010\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Significant work has been conducted in the domain of food computing, yet\\nthese studies typically focus on single tasks such as t2t (instruction\\ngeneration from food titles and ingredients), i2t (recipe generation from food\\nimages), or t2i (food image generation from recipes). None of these approaches\\nintegrate all modalities simultaneously. To address this gap, we introduce a\\nnovel food computing foundation model that achieves true multimodality,\\nencompassing tasks such as t2t, t2i, i2t, it2t, and t2ti. By leveraging large\\nlanguage models (LLMs) and pre-trained image encoder and decoder models, our\\nmodel can perform a diverse array of food computing-related tasks, including\\nfood understanding, food recognition, recipe generation, and food image\\ngeneration. Compared to previous models, our foundation model demonstrates a\\nsignificantly broader range of capabilities and exhibits superior performance,\\nparticularly in food image generation and recipe generation tasks. We\\nopen-sourced ChefFusion at GitHub.\",\"PeriodicalId\":501130,\"journal\":{\"name\":\"arXiv - CS - Computer Vision and Pattern Recognition\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Computer Vision and Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.12010\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Computer Vision and Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.12010","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

在食品计算领域已经开展了大量工作,但这些研究通常只关注单一任务,如 t2t(根据食品名称和配料生成指令)、i2t(根据食品图像生成食谱)或 t2i(根据食谱生成食品图像)。这些方法都无法同时整合所有模式。为了弥补这一不足,我们引入了一种高级食品计算基础模型,它可以实现真正的多模态,包括 t2t、t2i、i2t、it2t 和 t2ti 等任务。通过利用大型语言模型(LLMs)和预先训练好的图像编码器与解码器模型,我们的模型可以执行各种与食品计算相关的任务,包括食品理解、食品识别、食谱生成和食品图像生成。与以前的模型相比,我们的基础模型的功能范围明显更广,尤其在食品图像生成和食谱生成任务中表现出卓越的性能。我们在 GitHub 上开源了 ChefFusion。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ChefFusion: Multimodal Foundation Model Integrating Recipe and Food Image Generation
Significant work has been conducted in the domain of food computing, yet these studies typically focus on single tasks such as t2t (instruction generation from food titles and ingredients), i2t (recipe generation from food images), or t2i (food image generation from recipes). None of these approaches integrate all modalities simultaneously. To address this gap, we introduce a novel food computing foundation model that achieves true multimodality, encompassing tasks such as t2t, t2i, i2t, it2t, and t2ti. By leveraging large language models (LLMs) and pre-trained image encoder and decoder models, our model can perform a diverse array of food computing-related tasks, including food understanding, food recognition, recipe generation, and food image generation. Compared to previous models, our foundation model demonstrates a significantly broader range of capabilities and exhibits superior performance, particularly in food image generation and recipe generation tasks. We open-sourced ChefFusion at GitHub.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信