Efficient Visual Metaphor Image Generation Based on Metaphor Understanding

Author:

Su Chang,Wang Xingyue,Liu Shupin,Chen Yijiang

Abstract

AbstractMetaphor 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.

Publisher

Springer Science and Business Media LLC

Reference30 articles.

1. Hessel J, Marasović A, Hwang JD, Lee L, Da J, Zellers R, Mankoff R, Choi Y (2023) Do androids laugh at electric sheep? Humor “understanding” benchmarks from the new yorker caption contest

2. Yuri B, Simon D (2020) Sky + fire = sunset. exploring parallels between visually grounded metaphors and image classifiers. In: Beigman KB, Ekaterina S, Patricia L, Smaranda M, Chee W, Anna F, Debanjan G (eds) Proceedings of the second workshop on figurative language processing, pp 126–135, Online. Association for Computational Linguistics

3. Robin R, Andreas B, Dominik L, Patrick E, Björn O (2022) High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 10684–10695

4. Aditya R, Mikhail P, Gabriel G, Scott G, Chelsea V, Alec R, Mark C, Ilya S (2021) Zero-shot text-to-image generation. In: International conference on machine learning, pp 8821–8831. PMLR

5. Alex N, Prafulla D, Aditya R, Pranav S, Pamela M, Bob M, Ilya S, Mark C (2022) Glide: Towards photorealistic image generation and editing with text-guided diffusion models arxiv:2205.13168v1

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3