基于隐喻理解的高效视觉隐喻图像生成

IF 2.6 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neural Processing Letters Pub Date : 2024-04-16 DOI:10.1007/s11063-024-11609-w
Chang Su, Xingyue Wang, Shupin Liu, Yijiang Chen
{"title":"基于隐喻理解的高效视觉隐喻图像生成","authors":"Chang Su, Xingyue Wang, Shupin Liu, Yijiang Chen","doi":"10.1007/s11063-024-11609-w","DOIUrl":null,"url":null,"abstract":"<p>Metaphor has significant implications for revealing cognitive and thinking mechanisms. Visual metaphor image generation not only presents metaphorical connotations intuitively but also reflects AI’s understanding of metaphor through the generated images. This paper investigates the task of generating images based on text with visual metaphors. We explore metaphor image generation and create a dataset containing sentences with visual metaphors. Then, we propose a visual metaphor generation image framework based on metaphor understanding, which is more tailored to the essence of metaphor, better utilizes visual features, and has stronger interpretability. Specifically, the framework extracts the source domain, target domain, and metaphor interpretation from metaphorical sentences, separating the elements of the metaphor to deepen the understanding of its themes and intentions. Additionally, the framework introduces image data from the source domain to capture visual similarities and generate visual enhancement prompts specific to the domain. Finally, these prompts are combined with metaphorical interpretation sentences to form the final prompt text. Experimental results demonstrate that this approach effectively captures the essence of metaphor and generates metaphorical images consistent with the textual meaning.</p>","PeriodicalId":51144,"journal":{"name":"Neural Processing Letters","volume":"26 1","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Efficient Visual Metaphor Image Generation Based on Metaphor Understanding\",\"authors\":\"Chang Su, Xingyue Wang, Shupin Liu, Yijiang Chen\",\"doi\":\"10.1007/s11063-024-11609-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Metaphor has significant implications for revealing cognitive and thinking mechanisms. Visual metaphor image generation not only presents metaphorical connotations intuitively but also reflects AI’s understanding of metaphor through the generated images. This paper investigates the task of generating images based on text with visual metaphors. We explore metaphor image generation and create a dataset containing sentences with visual metaphors. Then, we propose a visual metaphor generation image framework based on metaphor understanding, which is more tailored to the essence of metaphor, better utilizes visual features, and has stronger interpretability. Specifically, the framework extracts the source domain, target domain, and metaphor interpretation from metaphorical sentences, separating the elements of the metaphor to deepen the understanding of its themes and intentions. Additionally, the framework introduces image data from the source domain to capture visual similarities and generate visual enhancement prompts specific to the domain. Finally, these prompts are combined with metaphorical interpretation sentences to form the final prompt text. Experimental results demonstrate that this approach effectively captures the essence of metaphor and generates metaphorical images consistent with the textual meaning.</p>\",\"PeriodicalId\":51144,\"journal\":{\"name\":\"Neural Processing Letters\",\"volume\":\"26 1\",\"pages\":\"\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-04-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neural Processing Letters\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s11063-024-11609-w\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Processing Letters","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11063-024-11609-w","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

隐喻对于揭示认知和思维机制具有重要意义。视觉隐喻图像生成不仅能直观地呈现隐喻内涵,还能通过生成的图像反映人工智能对隐喻的理解。本文研究了基于文本生成具有视觉隐喻的图像的任务。我们探讨了隐喻图像的生成,并创建了一个包含视觉隐喻句子的数据集。然后,我们提出了基于隐喻理解的视觉隐喻生成图像框架,该框架更符合隐喻的本质,能更好地利用视觉特征,并具有更强的可解释性。具体来说,该框架从隐喻句子中提取源域、目标域和隐喻解释,分离隐喻元素,加深对隐喻主题和意图的理解。此外,该框架还引入了源领域的图像数据,以捕捉视觉相似性,并生成该领域特有的视觉增强提示。最后,这些提示与隐喻解释句子相结合,形成最终的提示文本。实验结果表明,这种方法能有效捕捉隐喻的本质,并生成与文本含义一致的隐喻图像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Efficient Visual Metaphor Image Generation Based on Metaphor Understanding

Metaphor has significant implications for revealing cognitive and thinking mechanisms. Visual metaphor image generation not only presents metaphorical connotations intuitively but also reflects AI’s understanding of metaphor through the generated images. This paper investigates the task of generating images based on text with visual metaphors. We explore metaphor image generation and create a dataset containing sentences with visual metaphors. Then, we propose a visual metaphor generation image framework based on metaphor understanding, which is more tailored to the essence of metaphor, better utilizes visual features, and has stronger interpretability. Specifically, the framework extracts the source domain, target domain, and metaphor interpretation from metaphorical sentences, separating the elements of the metaphor to deepen the understanding of its themes and intentions. Additionally, the framework introduces image data from the source domain to capture visual similarities and generate visual enhancement prompts specific to the domain. Finally, these prompts are combined with metaphorical interpretation sentences to form the final prompt text. Experimental results demonstrate that this approach effectively captures the essence of metaphor and generates metaphorical images consistent with the textual meaning.

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
Neural Processing Letters
Neural Processing Letters 工程技术-计算机:人工智能
CiteScore
4.90
自引率
12.90%
发文量
392
审稿时长
2.8 months
期刊介绍: Neural Processing Letters is an international journal publishing research results and innovative ideas on all aspects of artificial neural networks. Coverage includes theoretical developments, biological models, new formal modes, learning, applications, software and hardware developments, and prospective researches. The journal promotes fast exchange of information in the community of neural network researchers and users. The resurgence of interest in the field of artificial neural networks since the beginning of the 1980s is coupled to tremendous research activity in specialized or multidisciplinary groups. Research, however, is not possible without good communication between people and the exchange of information, especially in a field covering such different areas; fast communication is also a key aspect, and this is the reason for Neural Processing Letters
期刊最新文献
Label-Only Membership Inference Attack Based on Model Explanation A Robot Ground Medium Classification Algorithm Based on Feature Fusion and Adaptive Spatio-Temporal Cascade Networks A Deep Learning-Based Hybrid CNN-LSTM Model for Location-Aware Web Service Recommendation A Clustering Pruning Method Based on Multidimensional Channel Information A Neural Network-Based Poisson Solver for Fluid Simulation
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1