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Text-to-Image (T2I) generative models have revolutionized content creation but remain highly sensitive to prompt phrasing, often requiring users to repeatedly refine prompts multiple times without clear feedback. While techniques such as…

Computer Vision and Pattern Recognition · Computer Science 2025-07-30 Chieh-Yun Chen , Min Shi , Gong Zhang , Humphrey Shi

Recent text-to-image (T2I) diffusion models produce visually stunning images and demonstrate excellent prompt following. But do they perform well as synthetic vision data generators? In this work, we revisit the promise of synthetic data as…

Computer Vision and Pattern Recognition · Computer Science 2026-04-21 Krzysztof Adamkiewicz , Brian Bernhard Moser , Stanislav Frolov , Tobias Christian Nauen , Federico Raue , Andreas Dengel

We present Infinite-Story, a training-free framework for consistent text-to-image (T2I) generation tailored for multi-prompt storytelling scenarios. Built upon a scale-wise autoregressive model, our method addresses two key challenges in…

Computer Vision and Pattern Recognition · Computer Science 2025-11-18 Jihun Park , Kyoungmin Lee , Jongmin Gim , Hyeonseo Jo , Minseok Oh , Wonhyeok Choi , Kyumin Hwang , Jaeyeul Kim , Minwoo Choi , Sunghoon Im

Contemporary Text-to-Image (T2I) models frequently depend on qualitative human evaluations to assess the consistency between synthesized images and the text prompts. There is a demand for quantitative and automatic evaluation tools, given…

Computer Vision and Pattern Recognition · Computer Science 2024-12-10 Ziyuan Qin , Dongjie Cheng , Haoyu Wang , Huahui Yi , Yuting Shao , Zhiyuan Fan , Kang Li , Qicheng Lao

How humans can effectively and efficiently acquire images has always been a perennial question. A classic solution is text-to-image retrieval from an existing database; however, the limited database typically lacks creativity. By contrast,…

Computer Vision and Pattern Recognition · Computer Science 2025-03-26 Leigang Qu , Haochuan Li , Tan Wang , Wenjie Wang , Yongqi Li , Liqiang Nie , Tat-Seng Chua

Although progress has been made for text-to-image synthesis, previous methods fall short of generalizing to unseen or underrepresented attribute compositions in the input text. Lacking compositionality could have severe implications for…

Computer Vision and Pattern Recognition · Computer Science 2022-03-30 Zhiheng Li , Martin Renqiang Min , Kai Li , Chenliang Xu

While text-to-image generation has achieved unprecedented fidelity, the vast majority of existing models function fundamentally as static text-to-pixel decoders. Consequently, they often fail to grasp implicit user intentions. Although…

Computer Vision and Pattern Recognition · Computer Science 2026-02-03 Jun He , Junyan Ye , Zilong Huang , Dongzhi Jiang , Chenjue Zhang , Leqi Zhu , Renrui Zhang , Xiang Zhang , Weijia Li

Unified multimodal generation architectures that jointly produce text and images have recently emerged as a promising direction for text-to-image (T2I) synthesis. However, many existing systems rely on explicit modality switching,…

Image-to-Image (I2I) translation is a heated topic in academia, and it also has been applied in real-world industry for tasks like image synthesis, super-resolution, and colorization. However, traditional I2I translation methods train data…

Computer Vision and Pattern Recognition · Computer Science 2020-10-28 Jialu Huang , Jing Liao , Sam Kwong

The goal of Image-to-image (I2I) translation is to transfer an image from a source domain to a target domain, which has recently drawn increasing attention. One major branch of this research is to formulate I2I translation based on…

Computer Vision and Pattern Recognition · Computer Science 2023-03-08 Yuanjiang Cao , Lina Yao , Le Pan , Quan Z. Sheng , Xiaojun Chang

Text-to-image (T2I) models have achieved remarkable progress, yet they continue to struggle with complex prompts that require simultaneously handling multiple objects, relations, and attributes. Existing inference-time strategies, such as…

Computer Vision and Pattern Recognition · Computer Science 2026-01-22 Shantanu Jaiswal , Mihir Prabhudesai , Nikash Bhardwaj , Zheyang Qin , Amir Zadeh , Chuan Li , Katerina Fragkiadaki , Deepak Pathak

Despite the impressive advances in text-to-image models, they often struggle to effectively compose complex scenes with multiple objects, displaying various attributes and relationships. To address this challenge, we present…

Computer Vision and Pattern Recognition · Computer Science 2025-03-19 Kaiyi Huang , Chengqi Duan , Kaiyue Sun , Enze Xie , Zhenguo Li , Xihui Liu

Diffusion models have emerged as a dominant paradigm for generative modeling across a wide range of domains, including prompt-conditional generation. The vast majority of samplers, however, rely on forward discretization of the reverse…

Computer Vision and Pattern Recognition · Computer Science 2025-12-01 Zhenghan Fang , Jian Zheng , Qiaozi Gao , Xiaofeng Gao , Jeremias Sulam

Customizing pre-trained text-to-image generation model has attracted massive research interest recently, due to its huge potential in real-world applications. Although existing methods are able to generate creative content for a novel…

Computer Vision and Pattern Recognition · Computer Science 2024-05-10 Yufan Zhou , Ruiyi Zhang , Jiuxiang Gu , Tong Sun

The incredible generative ability of large-scale text-to-image (T2I) models has demonstrated strong power of learning complex structures and meaningful semantics. However, relying solely on text prompts cannot fully take advantage of the…

Computer Vision and Pattern Recognition · Computer Science 2023-03-21 Chong Mou , Xintao Wang , Liangbin Xie , Yanze Wu , Jian Zhang , Zhongang Qi , Ying Shan , Xiaohu Qie

Symbolic world models (e.g., PDDL domains or executable simulators) are central to model-based planning, but training LLMs to generate such world models is limited by the lack of large-scale verifiable supervision. Current approaches rely…

Artificial Intelligence · Computer Science 2025-12-30 Mengkang Hu , Bowei Xia , Yuran Wu , Ailing Yu , Yude Zou , Qiguang Chen , Shijian Wang , Jiarui Jin , Kexin Li , Wenxiang Jiao , Yuan Lu , Ping Luo

Multilingual text-to-image (T2I) models have advanced rapidly in terms of visual realism and semantic alignment, and are now widely utilized. Yet outputs vary across cultural contexts: because language carries cultural connotations, images…

Computer Vision and Pattern Recognition · Computer Science 2025-11-24 Chuancheng Shi , Shangze Li , Shiming Guo , Simiao Xie , Wenhua Wu , Jingtong Dou , Chao Wu , Canran Xiao , Cong Wang , Zifeng Cheng , Fei Shen , Tat-Seng Chua

Text-to-image (T2I) generative models have recently emerged as a powerful tool, enabling the creation of photo-realistic images and giving rise to a multitude of applications. However, the effective integration of T2I models into…

Computer Vision and Pattern Recognition · Computer Science 2024-03-29 Zhicai Wang , Longhui Wei , Tan Wang , Heyu Chen , Yanbin Hao , Xiang Wang , Xiangnan He , Qi Tian

Text-to-image generative models have recently garnered significant attention due to their ability to generate images based on prompt descriptions. While these models have shown promising performance, concerns have been raised regarding the…

Computer Vision and Pattern Recognition · Computer Science 2025-09-09 Meiling Li , Zhenxing Qian , Xinpeng Zhang

Recent advancements in Text-to-Image (T2I) diffusion models have demonstrated impressive success in generating high-quality images with zero-shot generalization capabilities. Yet, current models struggle to closely adhere to prompt…

Computer Vision and Pattern Recognition · Computer Science 2024-01-31 Hyun Kang , Dohae Lee , Myungjin Shin , In-Kwon Lee
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