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With the rapid advancement of large multimodal models (LMMs), recent text-to-image (T2I) models can generate high-quality images and demonstrate great alignment to short prompts. However, they still struggle to effectively understand and…

Computer Vision and Pattern Recognition · Computer Science 2025-10-06 Juntong Wang , Huiyu Duan , Jiarui Wang , Ziheng Jia , Guangtao Zhai , Xiongkuo Min

Recent progress in text-to-image (T2I) generation underscores the importance of reliable benchmarks in evaluating how accurately generated images reflect the semantics of their textual prompt. However, (1) existing benchmarks lack the…

Computer Vision and Pattern Recognition · Computer Science 2026-02-25 Yibin Wang , Zhimin Li , Yuhang Zang , Jiazi Bu , Yujie Zhou , Yi Xin , Junjun He , Chunyu Wang , Qinglin Lu , Cheng Jin , Jiaqi Wang

Text-to-Image (T2I) models have made remarkable progress in generating images from text prompts, but their output quality and safety still depend heavily on how prompts are phrased. Existing safety methods typically refine prompts using…

Computer Vision and Pattern Recognition · Computer Science 2025-09-18 Jinwoo Jeon , JunHyeok Oh , Hayeong Lee , Byung-Jun Lee

Current text-to-image (T2I) generation models achieve promising results, but they fail on the scenarios where the knowledge implied in the text prompt is uncertain. For example, a T2I model released in February would struggle to generate a…

Computer Vision and Pattern Recognition · Computer Science 2025-05-22 Chuanhao Li , Jianwen Sun , Yukang Feng , Mingliang Zhai , Yifan Chang , Kaipeng Zhang

Text-to-image generation (T2I) refers to the text-guided generation of high-quality images. In the past few years, T2I has attracted widespread attention and numerous works have emerged. In this survey, we comprehensively review 141 works…

Computer Vision and Pattern Recognition · Computer Science 2025-05-06 Pengfei Yang , Ngai-Man Cheung , Xinda Ma

Despite recent advances in text-to-image (T2I) models, they often fail to faithfully render all elements of complex prompts, frequently omitting or misrepresenting specific objects and attributes. Test-time optimization has emerged as a…

Computer Vision and Pattern Recognition · Computer Science 2025-09-30 Mohammad Hossein Sameti , Amir M. Mansourian , Arash Marioriyad , Soheil Fadaee Oshyani , Mohammad Hossein Rohban , Mahdieh Soleymani Baghshah

While diffusion-based text-to-image (T2I) models provide a simple and powerful way to generate images, guiding this generation remains a challenge. For concepts that are difficult to describe through language, users may struggle to create…

Human-Computer Interaction · Computer Science 2023-08-11 John Joon Young Chung , Eytan Adar

Recent text-to-image (T2I) models have made remarkable progress in generating visually realistic and semantically coherent images. However, they still suffer from randomness and inconsistency with the given prompts, particularly when…

Computer Vision and Pattern Recognition · Computer Science 2026-03-31 Kaishen Wang , Ruibo Chen , Tong Zheng , Heng Huang

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

Text-to-image (T2I) models have advanced creative content generation, yet their reliance on large uncurated datasets often reproduces societal biases. We present FairT2I, a training-free and interactive framework grounded in a…

Computer Vision and Pattern Recognition · Computer Science 2026-01-08 Jinya Sakurai , Yuki Koyama , Issei Sato

TIPO (Text-to-Image Prompt Optimization) introduces an efficient approach for automatic prompt refinement in text-to-image (T2I) generation. Starting from simple user prompts, TIPO leverages a lightweight pre-trained model to expand these…

Computer Vision and Pattern Recognition · Computer Science 2026-02-10 Shih-Ying Yeh , Yi Li , Sang-Hyun Park , Giyeong Oh , Xuehai Wang , Min Song , Youngjae Yu , Shang-Hong Lai

While text-to-image (T2I) generative models have become ubiquitous, they do not necessarily generate images that align with a given prompt. While previous work has evaluated T2I alignment by proposing metrics, benchmarks, and templates for…

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

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

Recent advancements in text-to-image (T2I) generation have enabled models to produce high-quality images from textual descriptions. However, these models often struggle with complex instructions involving multiple objects, attributes, and…

Computer Vision and Pattern Recognition · Computer Science 2025-06-02 Yucheng Zhou , Jiahao Yuan , Qianning Wang

Text-to-image synthesis has made remarkable progress, yet accurately interpreting complex and lengthy prompts remains challenging, often resulting in semantic inconsistencies and missing details. Existing solutions, such as fine-tuning, are…

Computer Vision and Pattern Recognition · Computer Science 2025-10-09 Wen Ye , Zhaocheng Liu , Yuwei Gui , Tingyu Yuan , Yunyue Su , Bowen Fang , Chaoyang Zhao , Qiang Liu , Liang Wang

Text-to-image (T2I) models have garnered significant attention for generating high-quality images aligned with text prompts. However, rapid T2I model advancements reveal limitations in early benchmarks, lacking comprehensive evaluations,…

Computer Vision and Pattern Recognition · Computer Science 2025-06-27 Jingjing Chang , Yixiao Fang , Peng Xing , Shuhan Wu , Wei Cheng , Rui Wang , Xianfang Zeng , Gang Yu , Hai-Bao Chen

Current text-to-image (T2I) benchmarks evaluate models on rigid prompts, potentially underestimating true generative capabilities due to prompt sensitivity and creating biases that favor certain models while disadvantaging others. We…

Computer Vision and Pattern Recognition · Computer Science 2025-07-08 Haosheng Gan , Berk Tinaz , Mohammad Shahab Sepehri , Zalan Fabian , Mahdi Soltanolkotabi

Recent AI systems have shown extremely powerful performance, even surpassing human performance, on various tasks such as information retrieval, language generation, and image generation based on large language models (LLMs). At the same…

Artificial Intelligence · Computer Science 2024-05-29 Minseon Kim , Hyomin Lee , Boqing Gong , Huishuai Zhang , Sung Ju Hwang

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