Gotowe bibliografie tematyczne / Text-to-Image generation

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  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Książki
  4. Części książek
  5. Streszczenia konferencji
  6. Raporty organizacyjne

Gotowa bibliografia na temat „Text-to-Image generation”

Autor: Grafiati
Data publikacji: 2 czerwca 2025
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Text-to-Image generation"

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V, Pavithra, Rosy S, Srinishanthini R. B, and Prinslin L. "Text-To-Image Generation Using AI." International Journal of Research Publication and Reviews 4, no. 4 (2023): 4932–37. http://dx.doi.org/10.55248/gengpi.234.4.38568.

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Prasad, Timande Sahil Dhawale Shreyas Waghmare Sanket Dhumane Rushikesh Pimple Kshitij Madpuwar Dr. S. W. Mohod. "Text-to-Image Generation Using Deep Learning." International Journal of Advanced Innovative Technology in Engineering 10, no. 2 (2025): 113–15. https://doi.org/10.5281/zenodo.15407889.

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The Text-to-Image generation tasks has stand out as an innovation in the field of computer vision (CV). Recent innovations and advancements in generative models have led to the development of various text-to-image generation techniques. This paper presents a comprehensive implementation of a text to image generation system leveraging Stable Diffusion, a diffusion based generative model that can produce high-quality images with fine-grained details. The system is integrated with a Flask-based web application, providing users with a user-friendly interface to generate images from textual prompts
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Chaddha, Mahima, Sneha Kashid, and Snehal Bhosale Prof Radha Deoghare. "Deep Learning for X-ray Image to Text Generation." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (2019): 1679–82. http://dx.doi.org/10.31142/ijtsrd23168.

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Avhad, Pranjali. "WordCanvas: Text-to-Image Generation." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32152.

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This project investigates the novel use of stable dif- fusion techniques to generate high-quality images from detailed text descriptions. The combination of natural language under- standing and computer vision in text-to-image conversion opens up new possibilities for content creation and communication. Using cutting-edge stable diffusion models, our project builds a solid foundation for the generation process, which includes tokenization, pre-processing, specialized architecture design, and post-processing techniques. The advantages include eye-catching images, increased user engagement, cont
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Konarieva, I., D. Pydorenko, and O. Turuta. "A SURVEY OF METHODS OF TEXT-TO-IMAGE TRANSLATION." Bionics of Intelligence 2, no. 93 (2019): 64–68. http://dx.doi.org/10.30837/bi.2019.2(93).11.

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The given work considers the existing methods of text compression (finding keywords or creating summary) using RAKE, Lex Rank, Luhn, LSA, Text Rank algorithms; image generation; text-to-image and image-to-image translation including GANs (generative adversarial networks). Different types of GANs were described such as StyleGAN, GauGAN, Pix2Pix, CycleGAN, BigGAN, AttnGAN. This work aims to show ways to create illustrations for the text. First, key information should be obtained from the text. Second, this key information should be transformed into images. There were proposed several ways to tra
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Li, Jia, Lijie Hu, Jingfeng Zhang, Tianhang Zheng, Hua Zhang, and Di Wang. "Fair Text-to-Image Diffusion via Fair Mapping." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 25 (2025): 26256–64. https://doi.org/10.1609/aaai.v39i25.34823.

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In this paper, we address the limitations of existing text-to-image diffusion models in generating demographically fair results when given human-related descriptions. These models often struggle to disentangle the target language context from sociocultural biases, resulting in biased image generation. To overcome this challenge, we propose Fair Mapping, a flexible, model-agnostic, and lightweight approach that modifies a pre-trained text-to-image diffusion model by controlling the prompt to achieve fair image generation. One key advantage of our approach is its high efficiency. It only require
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S A, Priyanka. "Dynamic Visual Creation: Implementing Text to Image Generation." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48652.

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Abstract—Generative Adversarial Network (GANs) has become one of the most interesting ideas in the last years in Machine Learning. Generative Adversarial Network is a very exciting area and that’s why researchers are so excited about building generative models as they are set to vary what machines can do for humans. This paper proposes the generation of realistic images according to their semantics based on text description using a Knowledge Graph alongside Knowledge Guided Generative Adversarial Network (KG-GAN) that comes with the embeddings generated from the Knowledge Graph (KG) into GAN.
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Tewel, Yoad, Omri Kaduri, Rinon Gal, et al. "Training-Free Consistent Text-to-Image Generation." ACM Transactions on Graphics 43, no. 4 (2024): 1–18. http://dx.doi.org/10.1145/3658157.

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Text-to-image models offer a new level of creative flexibility by allowing users to guide the image generation process through natural language. However, using these models to consistently portray the same subject across diverse prompts remains challenging. Existing approaches fine-tune the model to teach it new words that describe specific user-provided subjects or add image conditioning to the model. These methods require lengthy persubject optimization or large-scale pre-training. Moreover, they struggle to align generated images with text prompts and face difficulties in portraying multipl
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Liao, Jiayi, Xu Chen, Qiang Fu, et al. "Text-to-Image Generation for Abstract Concepts." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (2024): 3360–68. http://dx.doi.org/10.1609/aaai.v38i4.28122.

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Recent years have witnessed the substantial progress of large-scale models across various domains, such as natural language processing and computer vision, facilitating the expression of concrete concepts. Unlike concrete concepts that are usually directly associated with physical objects, expressing abstract concepts through natural language requires considerable effort since they are characterized by intricate semantics and connotations. An alternative approach is to leverage images to convey rich visual information as a supplement. Nevertheless, existing Text-to-Image (T2I) models are prima
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Liu, An-An, Zefang Sun, Ning Xu, et al. "Prior knowledge guided text to image generation." Pattern Recognition Letters 177 (January 2024): 89–95. http://dx.doi.org/10.1016/j.patrec.2023.12.003.

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Rozprawy doktorskie na temat "Text-to-Image generation"

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Bejiga, Mesay Belete. "Adversarial approaches to remote sensing image analysis." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/257100.

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The recent advance in generative modeling in particular the unsupervised learning of data distribution is attributed to the invention of models with new learning algorithms. Among the methods proposed, generative adversarial networks (GANs) have shown to be the most efficient approaches to estimate data distributions. The core idea of GANs is an adversarial training of two deep neural networks, called generator and discriminator, to learn an implicit approximation of the true data distribution. The distribution is approximated through the weights of the generator network, and interaction with
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Bejiga, Mesay Belete. "Adversarial approaches to remote sensing image analysis." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/257100.

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The recent advance in generative modeling in particular the unsupervised learning of data distribution is attributed to the invention of models with new learning algorithms. Among the methods proposed, generative adversarial networks (GANs) have shown to be the most efficient approaches to estimate data distributions. The core idea of GANs is an adversarial training of two deep neural networks, called generator and discriminator, to learn an implicit approximation of the true data distribution. The distribution is approximated through the weights of the generator network, and interaction with
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Książki na temat "Text-to-Image generation"

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Klyuchevskaya, Irina, Ol'ga Semikova, and Igor' Zheleznov. Advertising and PR activities of a hotel company. INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1852505.

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The textbook examines the content and specifics of the advertising and PR activities of a hotel enterprise related to the peculiarities of the development of the market of hotel services and hotel products, analyzes advertising and PR tools and strategies aimed at ensuring the successful positioning of a hotel enterprise in the market. The issues of the organization of advertising and PR activities of hotels are highlighted, the issues of the use of channels for the distribution of advertising appeals and PR materials, the use of Internet technologies in advertising, the principles of creating
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Lombardi, Elena. Women as Text, Text as Woman. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198818960.003.0004.

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This chapter explores an ancient cultural theme: the links between women and textuality, involving images of female nudity (‘the naked truth’) and of clothing and make up (ornatus). It begins by exploring the mechanics of reading as a homoerotic exchange between a male author and a male reader through the guise of a personified female text, and it documents the evolution of such a theme in the age of Dante, with the flourishing of a rather independent form of female lyric textuality. In creating an embodied text that moves about, speaks, cries out in joy or sorrow, but also writes and reads it
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Shamma, Yasmine. Joe Brainard’s Collaged Spaces. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198808725.003.0003.

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Brainard was not only an illustrator and friend to many New York School poets, he was also an avid letter writer, collage artist, miniature artist, cartoonist, and serious poet. How is contemporary poetry involved in an overlooked dialogue with collage art? This chapter suggests a general tendency towards assembly across the disciplines of text and image which govern both first- and second-generation New York School aesthetics. This chapter showcases how Brainard’s work instigates and propels the collaged poetry of The New York Schools from the real and influential side-lines of their poems? T
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Hale, Meredith McNeill. The Birth of Modern Political Satire. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198836261.001.0001.

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This book documents one of the most important moments in the history of printed political imagery, when the political print became what we would recognize as modern political satire. Contrary to conventional historical and art-historical narratives, which place the emergence of political satire in the news-driven coffee-house culture of eighteenth-century London, this study locates the birth of the genre in the late seventeenth-century Netherlands in the contentious political milieu surrounding William III’s invasion of England known as the ‘Glorious Revolution’. The satires produced between 1
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Cannon, Jeffrey G. The Image as Text and Context in Early Mormon History. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190274375.003.0012.

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In “The Image as Text and Context in Early Mormon History,” Jeffrey G. Cannon examines the creation of images that capture aspects of the foundational period of Mormonism though made in subsequent decades. Understanding the context for the creation of these images facilitates a discernment of their full meaning to later generations. While very few contemporary images of early Mormonism exist, later attempts to document or illustrate the movement’s origins inform the Mormon attitudes of their own past. Cannon looks at several frequently used photographs or images and the context in which they w
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Brantingham, Patricia L., Paul J. Brantingham, Justin Song, and Valerie Spicer. Advances in Visualization for Theory Testing in Environmental Criminology. Edited by Gerben J. N. Bruinsma and Shane D. Johnson. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190279707.013.37.

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This chapter discusses advances in visualization for environmental criminology. The environment within which people move has many dimensions that influence or constrain decisions and actions by individuals and by groups. This complexity creates a challenge for theoreticians and researchers in presenting their research results in a way that conveys the dynamic spatiotemporal aspects of crime and actions by offenders in a clearly understandable way. There is an increasing need in environmental criminology to use scientific visualization to convey research results. A visual image can describe und
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Barrett, Chris. Time River Body. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198816874.003.0003.

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Relying on sustained, relentless personification, Drayton’s chorographical epic Poly-Olbion retains the ethical work of The Faerie Queene’s allegory in restoring to the national narrative the embodied immanence of the people within it; but Drayton’s poem takes personification as its dominant representation mode. The poem explores and exploits the paradoxical nature of personification, a device that is simultaneously evocative and anti-mimetic. The case of personification demonstrates the ways description distances itself from its subject, and Poly-Olbion uses its personified topography to inte
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Ufimtseva, Nataliya V., Iosif A. Sternin, and Elena Yu Myagkova. Russian psycholinguistics: results and prospects (1966–2021): a research monograph. Institute of Linguistics, Russian Academy of Sciences, 2021. http://dx.doi.org/10.30982/978-5-6045633-7-3.

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The monograph reflects the problems of Russian psycholinguistics from the moment of its inception in Russia to the present day and presents its main directions that are currently developing. In addition, theoretical developments and practical results obtained in the framework of different directions and research centers are described in a concise form. The task of the book is to reflect, as far as it is possible in one edition, firstly, the history of the formation of Russian psycholinguistics; secondly, its methodology and developed methods; thirdly, the results obtained in different research
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Części książek na temat "Text-to-Image generation"

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Solanki, Shivam R., and Drupad K. Khublani. "Text-to-Image Generation." In Generative Artificial Intelligence. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0403-8_2.

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Lin, Zhiqiu, Deepak Pathak, Baiqi Li, et al. "Evaluating Text-to-Visual Generation with Image-to-Text Generation." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72673-6_20.

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Oppenlaender, Jonas. "The Cultivated Practices of Text-to-Image Generation." In Humane Autonomous Technology. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-66528-8_14.

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AbstractHumankind is entering a novel creative era in which anybody can synthesise digital information using generative artificial intelligence (AI). Text-to-image generation, in particular, has become vastly popular and millions of practitioners produce AI-generated images and AI art online. This chapter first gives an overview of the key developments that enabled a healthy co-creative online ecosystem around text-to-image generation to rapidly emerge, followed by a high-level description of key elements in this ecosystem. A particular focus is placed on prompt engineering, a creative practic
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Yan, Kun, Lei Ji, Chenfei Wu, et al. "Trace Controlled Text to Image Generation." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20059-5_4.

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Jindal, Rajni, V. Sriram, Vishesh Aggarwal, and Vishesh Jain. "Text to Image Generation Using Gan." In Pervasive Computing and Social Networking. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2840-6_51.

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Liu, Sifei, Weili Nie, An-Chieh Cheng, et al. "Compositional Text-to-Image Generation with Feedforward Layout Generation." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-91979-4_3.

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Deng, Zhiyu, Wenxin Yu, Lu Che, et al. "Text to Image Generation with Conformer-GAN." In Neural Information Processing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8073-4_1.

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Zhao, Liang, Xinwei Li, Pingda Huang, Zhikui Chen, Yanqi Dai, and Tianyu Li. "TRGAN: Text to Image Generation Through Optimizing Initial Image." In Communications in Computer and Information Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92307-5_76.

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Rao, Supriya B., Shailesh S. Shetty, Chandra Singh, Srinivas P. M, and Anush Bekal. "Text to Image Generation Using Attentional Generative Adversarial Network." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-47221-3_7.

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Chen, Yarui, Fang Bao, Jianlin Shao, Jucheng Yang, Yuan Wang, and Tingting Zhao. "Text to Image Generation Based on Adaptive Attention." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-96-0125-7_11.

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Streszczenia konferencji na temat "Text-to-Image generation"

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Vinothkumar, S., S. Varadhaganapathy, R. Shanthakumari, S. Dhanushya, S. Guhan, and P. Krisvanth. "Utilizing Generative AI for Text-to-Image Generation." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725454.

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Chandila, Rishabh, and Deepak Kumar. "Conversion of Text-to-Image Generation." In 2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT). IEEE, 2024. http://dx.doi.org/10.1109/iceect61758.2024.10739305.

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Haydarov, Kilichbek, Aashiq Muhamed, Xiaoqian Shen, et al. "Adversarial Text to Continuous Image Generation." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.00604.

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Xu, Ning, and Serhad Doken. "Automatic Visual Citation Generation for Text-to-Image Generation." In 2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE, 2024. http://dx.doi.org/10.1109/mipr62202.2024.00015.

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Zhou, Yufan, Ruiyi Zhang, Jiuxiang Gu, and Tong Sun. "Customization Assistant for Text-to-image Generation." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.00877.

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Höllein, Lukas, Aljaž Božič, Norman Müller, et al. "ViewDiff: 3D-Consistent Image Generation with Text-to-Image Models." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.00482.

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Zhan, Jingtao, Qingyao Ai, Yiqun Liu, et al. "Prompt Refinement with Image Pivot for Text-to-Image Generation." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.53.

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Sun, Jiao, Deqing Fu, Yushi Hu, et al. "DreamSync: Aligning Text-to-Image Generation with Image Understanding Feedback." In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.naacl-long.304.

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Datta, Siddhartha, Alexander Ku, Deepak Ramachandran, and Peter Anderson. "Prompt Expansion for Adaptive Text-to-Image Generation." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.189.

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Ramzan, Hafiz Arslan, Sadia Ramzan, Tehmina Kalsum, Minahil Rauf, and Maria Sehar. "Text-to-Image Generation Using Recurrent Convolutional GANs." In 2025 International Conference on Emerging Technologies in Electronics, Computing, and Communication (ICETECC). IEEE, 2025. https://doi.org/10.1109/icetecc65365.2025.11070226.

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Raporty organizacyjne na temat "Text-to-Image generation"

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Ampatzidis, Yiannis, Mahendra Bhandari, Andres Ferreyra, et al. AI in Agriculture: Opportunities, Challenges, and Recommendations. Chair Alex Thomasson. Council for Agricultural Science and Technology, 2025. https://doi.org/10.62300/iaag042514.

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Artificial Intelligence (AI) is rapidly being integrated into people’s lives, reshaping industries, and enabling previously unimagined innovation, even in agriculture. Generative AI focuses on creating content like text and pictures based on vast quantities of data. ExtensionBot is a generative AI platform that supports agricultural extension by providing farmers with accurate scientific information and specific recommendations. It has been shown to deliver more accurate responses to agricultural questions than broader generative AI models. Other forms of AI have been used to analyze data to p
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Foundation models such as ChatGPT through the prism of the UNESCO Recommendation on the Ethics of Artificial Intelligence. UNESCO, 2023. http://dx.doi.org/10.54678/bgiv6160.

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The release into the public domain and massive growth in the user base of artificial intelligence (AI) foundation models for text, images, and audio is fuelling debate about the risks they pose to work, education, scientific research, and democracy, as well as their potential negative impacts on cultural diversity and cross-cultural interactions, among other areas. Foundation models are AI systems that are characterized by the use of very large machine learning models trained on massive unlabelled data sets using considerable compute resources. Examples include large language models (LLMs) suc
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