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Related papers: Design Guidelines for Prompt Engineering Text-to-I…

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Text-to-image generative models excel in creating images from text but struggle with ensuring alignment and consistency between outputs and prompts. This paper introduces TextMatch, a novel framework that leverages multimodal optimization…

Computer Vision and Pattern Recognition · Computer Science 2025-01-28 Yucong Luo , Mingyue Cheng , Jie Ouyang , Xiaoyu Tao , Qi Liu

Text-to-Image (TTI) models are powerful creative tools but risk amplifying harmful social biases. We frame representational societal bias assessment as an image curation and evaluation task and introduce a pilot benchmark of occupational…

Computation and Language · Computer Science 2025-09-03 Shaina Raza , Maximus Powers , Partha Pratim Saha , Mahveen Raza , Rizwan Qureshi

Recommender Systems are built to retrieve relevant items to satisfy users' information needs. The candidate corpus usually consists of a finite set of items that are ready to be served, such as videos, products, or articles. With recent…

Information Retrieval · Computer Science 2023-12-08 Yuanhe Guo , Haoming Liu , Hongyi Wen

The impressive capacity shown by recent text-to-image diffusion models to generate high-quality pictures from textual input prompts has leveraged the debate about the very definition of art. Nonetheless, these models have been trained using…

Computation and Language · Computer Science 2022-10-20 Ricardo Kleinlein , Cristina Luna-Jiménez , Fernando Fernández-Martínez

Previous work on augmenting large multimodal models (LMMs) for text-to-image (T2I) generation has focused on enriching the input space of in-context learning (ICL). This includes providing a few demonstrations and optimizing image…

Computation and Language · Computer Science 2025-01-14 Yongyu Mu , Hengyu Li , Junxin Wang , Xiaoxuan Zhou , Chenglong Wang , Yingfeng Luo , Qiaozhi He , Tong Xiao , Guocheng Chen , Jingbo Zhu

We present a method to control a text-to-image generative model to produce training data useful for supervised learning. Unlike previous works that employ an open-loop approach and pre-define prompts to generate new data using either a…

Computer Vision and Pattern Recognition · Computer Science 2025-05-20 Teresa Yeo , Andrei Atanov , Harold Benoit , Aleksandr Alekseev , Ruchira Ray , Pooya Esmaeil Akhoondi , Amir Zamir

The recent progress of text-to-image generation has been recognized in architectural design. Our study is the first to investigate the potential of text-to-image generators in supporting creativity during the early stages of the…

Human-Computer Interaction · Computer Science 2024-01-17 Ville Paananen , Jonas Oppenlaender , Aku Visuri

Recent advances in text-to-image (T2I) generation have achieved impressive results, yet existing models often struggle with simple or underspecified prompts, leading to suboptimal image-text alignment, aesthetics, and quality. We propose a…

Computation and Language · Computer Science 2025-10-16 Ruibo Chen , Jiacheng Pan , Heng Huang , Zhenheng Yang

Creativity is a valuable human skill that has long been augmented through both analog and digital tools. Recent progress in generative AI, such as image generation, provides a disruptive technological solution to supporting human creativity…

Human-Computer Interaction · Computer Science 2025-06-05 Daniel Hove Paludan , Julie Fredsgård , Kasper Patrick Bährentz , Ilhan Aslan , Niels van Berkel

Well-designed prompts have demonstrated the potential to guide text-to-image models in generating amazing images. Although existing prompt engineering methods can provide high-level guidance, it is challenging for novice users to achieve…

Multimedia · Computer Science 2026-03-27 Nailei Hei , Qianyu Guo , Zihao Wang , Yan Wang , Haofen Wang , Wenqiang Zhang

In the world of technology and AI, digital models play an important role in our lives and are an essential part of the digital twins of real-world objects. They can be created by designers, artists, or game developers using spline curves…

Graphics · Computer Science 2025-05-20 Rushan Ziatdinov , Rifkat Nabiyev

Diffusion-based generative models have significantly advanced text-to-image generation but encounter challenges when processing lengthy and intricate text prompts describing complex scenes with multiple objects. While excelling in…

Computer Vision and Pattern Recognition · Computer Science 2024-02-27 Hanan Gani , Shariq Farooq Bhat , Muzammal Naseer , Salman Khan , Peter Wonka

Text-to-image generative models, specifically those based on diffusion models like Imagen and Stable Diffusion, have made substantial advancements. Recently, there has been a surge of interest in the delicate refinement of text prompts.…

Computer Vision and Pattern Recognition · Computer Science 2024-04-08 Wenyi Mo , Tianyu Zhang , Yalong Bai , Bing Su , Ji-Rong Wen , Qing Yang

Large Language Models (LLMs) have shown prominent performance in various downstream tasks and prompt engineering plays a pivotal role in optimizing LLMs' performance. This paper, not only as an overview of current prompt engineering…

Computation and Language · Computer Science 2024-09-18 Haochen Li , Jonathan Leung , Zhiqi Shen

Text-to-image generative models are capable of producing high-quality images that often faithfully depict concepts described using natural language. In this work, we comprehensively evaluate a range of text-to-image models on numerical…

Machine Learning · Computer Science 2025-02-07 Ivana Kajić , Olivia Wiles , Isabela Albuquerque , Matthias Bauer , Su Wang , Jordi Pont-Tuset , Aida Nematzadeh

While text-to-image (T2I) models can synthesize high-quality images, their performance degrades significantly when prompted with novel or out-of-distribution (OOD) entities due to inherent knowledge cutoffs. We introduce World-To-Image, a…

Computer Vision and Pattern Recognition · Computer Science 2025-10-07 Moo Hyun Son , Jintaek Oh , Sun Bin Mun , Jaechul Roh , Sehyun Choi

Authoring realistic haptic textures typically requires low-level parameter tuning and repeated trial-and-error, limiting speed, transparency, and creative reach. We present a language-driven authoring system that turns natural-language…

Human-Computer Interaction · Computer Science 2026-04-09 Wanli Qian , Aiden Chang , Shihan Lu , Michael Gu , Heather Culbertson

While most research on controllable text generation has focused on steering base Language Models, the emerging instruction-tuning and prompting paradigm offers an alternate approach to controllability. We compile and release ConGenBench, a…

Computation and Language · Computer Science 2024-05-03 Dhananjay Ashok , Barnabas Poczos

With the advancement in generative language models, the selection of prompts has gained significant attention in recent years. A prompt is an instruction or description provided by the user, serving as a guide for the generative language…

Machine Learning · Statistics 2024-05-21 Haoting Zhang , Jinghai He , Rhonda Righter , Zeyu Zheng

Text to image generation methods (T2I) are widely popular in generating art and other creative artifacts. While visual hallucinations can be a positive factor in scenarios where creativity is appreciated, such artifacts are poorly suited…

Computer Vision and Pattern Recognition · Computer Science 2023-05-30 Rodrigo Valerio , Joao Bordalo , Michal Yarom , Yonatan Bitton , Idan Szpektor , Joao Magalhaes
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