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Related papers: Visual Prompting with Iterative Refinement for Des…

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Despite recent progress in text-to-image (T2I) generation, existing models often struggle to faithfully capture user intentions from short and under-specified prompts. While prior work has attempted to enhance prompts using large language…

Computer Vision and Pattern Recognition · Computer Science 2025-05-26 Mingrui Wu , Lu Wang , Pu Zhao , Fangkai Yang , Jianjin Zhang , Jianfeng Liu , Yuefeng Zhan , Weihao Han , Hao Sun , Jiayi Ji , Xiaoshuai Sun , Qingwei Lin , Weiwei Deng , Dongmei Zhang , Feng Sun , Qi Zhang , Rongrong Ji

Recent developments in large language models (LLM) and generative AI have unleashed the astonishing capabilities of text-to-image generation systems to synthesize high-quality images that are faithful to a given reference text, known as a…

Human-Computer Interaction · Computer Science 2023-03-17 Yutong Xie , Zhaoying Pan , Jinge Ma , Luo Jie , Qiaozhu Mei

In automated UI design generation, a key challenge is the lack of support for iterative processes, as most systems focus solely on end-to-end output. This stems from limited capabilities in interpreting design intent and a lack of…

Human-Computer Interaction · Computer Science 2025-10-30 Mingyue Yuan , Jieshan Chen , Yongquan Hu , Sidong Feng , Mulong Xie , Gelareh Mohammadi , Zhenchang Xing , Aaron Quigley

Text-to-image models are powerful for producing high-quality images based on given text prompts, but crafting these prompts often requires specialized vocabulary. To address this, existing methods train rewriting models with supervision…

Computer Vision and Pattern Recognition · Computer Science 2025-12-16 Hongji Yang , Yucheng Zhou , Wencheng Han , Jianbing Shen

Large Language Models (LLMs) are increasingly applied to automate software engineering tasks, including the generation of UML class diagrams from natural language descriptions. While prior work demonstrates that LLMs can produce…

Software Engineering · Computer Science 2026-04-07 Rabia Iftikhar , Andreas Rausch

Large Language Models (LLMs) often generate substantively relevant content but fail to adhere to formal constraints, leading to outputs that are conceptually correct but procedurally flawed. Traditional prompt refinement approaches focus on…

Artificial Intelligence · Computer Science 2026-01-08 Alberto Purpura , Li Wang , Sahil Badyal , Eugenio Beaufrand , Adam Faulkner

Large language models (LLMs) have become increasingly capable of following instructions and complex reasoning, making prompting a flexible interface for adapting models without parameter updates. Yet prompt design remains labor-intensive…

Computation and Language · Computer Science 2026-05-22 Farima Fatahi Bayat , Moin Aminnaseri , Pouya Pezeshkpour , Estevam Hruschka

This paper assesses the potential for large language models (LLMs) to serve as assistive tools in the creative writing process, by means of a single, in-depth case study. In the course of the study, we develop interactive and multi-voice…

Computation and Language · Computer Science 2023-12-08 Murray Shanahan , Catherine Clarke

Generative AI (GenAI) tools are increasingly integrated into design workflows. While text prompts remain the primary input method for GenAI image tools, designers often struggle to craft effective ones. Moreover, research has primarily…

Human-Computer Interaction · Computer Science 2025-04-09 Hyerim Park , Malin Eiband , Andre Luckow , Michael Sedlmair

Large Language Models (LLMs) have become indispensable for evaluating writing. However, text feedback they provide is often unintelligible, generic, and not specific to user criteria. Inspired by structured rubrics in education and…

Human-Computer Interaction · Computer Science 2026-02-16 Jingwen Bai , Wei Soon Cheong , Philippe Muller , Brian Y Lim

Environment designers in the entertainment industry create imaginative 2D and 3D scenes for games, films, and television, requiring both fine-grained control of specific details and consistent global coherence. Designers have increasingly…

Human-Computer Interaction · Computer Science 2025-09-03 Wen-Fan Wang , Ting-Ying Lee , Chien-Ting Lu , Che-Wei Hsu , Nil Ponsa Campanyà , Yu Chen , Mike Y. Chen , Bing-Yu Chen

The notable gap between user-provided and model-preferred prompts poses a significant challenge for generating high-quality images with text-to-image models, compelling the need for prompt engineering. Current studies on prompt engineering…

Computer Vision and Pattern Recognition · Computer Science 2026-03-09 Shiyu Wu , Mingzhen Sun , Weining Wang , Yequan Wang , Jing Liu

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

Prompt engineering is crucial for achieving reliable and effective outputs from large language models (LLMs), but its design requires specialized knowledge of prompting techniques and a deep understanding of target tasks. To address this…

Computation and Language · Computer Science 2025-10-22 Yohei Ikenoue , Hitomi Tashiro , Shigeru Kuroyanagi

Prompt-driven image analysis converts a single natural-language instruction into multiple steps: locate, segment, edit, and describe. We present a practical case study of a unified pipeline that combines open-vocabulary detection,…

Computer Vision and Pattern Recognition · Computer Science 2025-09-11 Kaleem Ahmad

Large Language Models (LLMs) have been shown to enhance the effectiveness of enriching item descriptions, thereby improving the accuracy of recommendation systems. However, most existing approaches either rely on text-only prompting or…

Information Retrieval · Computer Science 2025-10-24 Hanjia Lyu , Ryan Rossi , Xiang Chen , Md Mehrab Tanjim , Stefano Petrangeli , Somdeb Sarkhel , Jiebo Luo

Traditional image annotation tasks rely heavily on human effort for object selection and label assignment, making the process time-consuming and prone to decreased efficiency as annotators experience fatigue after extensive work. This paper…

Computer Vision and Pattern Recognition · Computer Science 2025-03-17 He Zhang , Xinyi Fu , John M. Carroll

Current research has explored how Generative AI can support the brainstorming process for content creators, but a gap remains in exploring support-tools for the pre-writing process. Specifically, our research is focused on supporting users…

Human-Computer Interaction · Computer Science 2024-06-19 Grace Li , Tao Long , Lydia B. Chilton

Text-to-image (T2I) generation models have made significant strides but still struggle with prompt sensitivity: even minor changes in prompt wording can yield inconsistent or inaccurate outputs. To address this challenge, we introduce a…

Machine Learning · Computer Science 2025-07-31 Mohammad Abdul Hafeez Khan , Yash Jain , Siddhartha Bhattacharyya , Vibhav Vineet

Creative generation is the synthesis of new, surprising, and valuable samples that reflect user intent yet cannot be envisioned in advance. This task aims to extend human imagination, enabling the discovery of visual concepts that exist in…

Graphics · Computer Science 2025-10-14 Shelly Golan , Yotam Nitzan , Zongze Wu , Or Patashnik