mlsanityData to 14 Oct 2026

Semantic Draw Engineering for Text-to-Image Creation

arXiv 2401.04116 ↗First posted 23 Dec 2023cs.HC cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Text-to-image generation is conducted through Generative Adversarial Networks (GANs) or transformer models. However, the current challenge lies in accurately generating images based on textual descriptions, especially in scenarios where the content and theme of the target image are ambiguous. In this paper, we propose a method that utilizes artificial intelligence models for thematic creativity, followed by a classification modeling of the actual painting process. The method involves converting all visual elements into quantifiable data structures before creating images. We evaluate the effectiveness of this approach in terms of semantic accuracy, image reproducibility, and computational efficiency, in comparison with existing image generation algorithms.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 33 references that are in the map. First posted on arXiv 23 Dec 2023.

Builds on

24 of 33 references.

References · 24

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  3. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
  4. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,614
  5. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,626
  6. 2017Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answeringcited by 1,312
  7. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,256
  8. 2017Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environmentscited by 1,027
  9. 2019How Can We Know What Language Models Know?cited by 959
  10. 2014Show and Tell: A Neural Image Caption Generatorcited by 879
  11. 2016Generative Adversarial Text to Image Synthesiscited by 735
  12. 2020It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learnerscited by 669
12 more
  1. 2017AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networkscited by 664
  2. 2016StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networkscited by 577
  3. 2022DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Modelscited by 379
  4. 2019Meshed-Memory Transformer for Image Captioningcited by 343
  5. 2019DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesiscited by 308
  6. 2021Design Guidelines for Prompt Engineering Text-to-Image Generative Modelscited by 218
  7. 2022PromptChainer: Chaining Large Language Model Prompts through Visual Programmingcited by 134
  8. 2018Neural Baby Talkcited by 107
  9. 2022Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Modelscited by 70
  10. 2022A Taxonomy of Prompt Modifiers for Text-To-Image Generationcited by 55
  11. 2023Towards an Understanding and Explanation for Mixed-Initiative Artificial Scientific Text Detectioncited by 8
  12. 2022DPVisCreator: Incorporating Pattern Constraints to Privacy-preserving Visualizations via Differential Privacycited by 4

Built on by

Not cited yet.