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title: "Who cites High-Resolution — page 2 — 1,262 citing papers"
description: "1,262 papers of the Elamur Research library cite «High-Resolution Image Synthesis with Latent Diffusion Models», found in their…"
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# Who cites «High-Resolution Image Synthesis with Latent Diffusion Models»

1,262 papers of our library cite it (of the 149,439 whose bibliographies we read) · 122 with a page here, the most cited first · page 2 of 2 · 101–122 · [← back to «High-Resolution Image Synthesis with Latent Diffusion Models»](https://md.research.elamur.ai/free/en/papers/rombach-2021-high-resolution-image-synthesis-latent/)

[← prev](https://md.research.elamur.ai/free/en/papers/rombach-2021-high-resolution-image-synthesis-latent/cited-by/) [1](https://md.research.elamur.ai/free/en/papers/rombach-2021-high-resolution-image-synthesis-latent/cited-by/) **2**

101. **[Detecting AI-Generated Content on Social Media with Multi-modal Language Models](https://md.research.elamur.ai/free/en/papers/yang-2026-detecting-ai-generated-content-social/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chenyang Yang, Shen Yan, Yibo Yang, et al.*2026*
102. **[Diff4TST: Masked Diffusion Language Model for Text Style Transfer](https://md.research.elamur.ai/free/en/papers/ma-2026-diff4tst-masked-diffusion-language-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinchen Ma, Gaole He, Yunshi Lan, et al.*2026*
103. **[Do NOT Classify and Count: Hybrid Attribute Control Success Evaluation](https://md.research.elamur.ai/free/en/papers/saaro-2026-do-not-classify-count-hybrid/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Felix Matthias Saaro, Pius von Däniken, Mark Cieliebak, et al.*2026*
104. **[FactVerse: A Benchmark for Factual Consistency in Interleaved Image–Text Generation](https://md.research.elamur.ai/free/en/papers/shan-2026-factverse-benchmark-factual-consistency-interleaved/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yubo Shan, Kun Zhang, Qiming Xu, et al.*2026*
105. **[How Do Inpainting Artifacts Propagate to Language?](https://md.research.elamur.ai/free/en/papers/yashwante-2026-how-do-inpainting-artifacts-propagate/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Pratham Yashwante, Davit Abrahamyan, Shresth Grover, et al.*2026*
106. **[ImmersiveTTS: Environment-Aware Text-to-Speech with Multimodal Diffusion Transformer and Domain-Specific Representation Alignment](https://md.research.elamur.ai/free/en/papers/yun-2026-immersivetts-environment-aware-text-speech/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jun-Hak Yun, Seung-Bin Kim, Seong-Whan Lee*2026*
107. **[Language-Grounded Multi-Domain Image Translation via Semantic Difference Guidance](https://md.research.elamur.ai/free/en/papers/ryu-2026-language-grounded-multi-domain-image/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Jongwon Ryu, Joonhyung Park, Jaeho Han, et al.*2026*
108. **[Large-Scale Multimodal Knowledge Graph about Classical Chinese Poetry: Fine-grained Method and Comprehensive Evaluation](https://md.research.elamur.ai/free/en/papers/wang-2026-large-scale-multimodal-knowledge-graph/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shuo Wang, Qing Zhu, Yang Xiao, et al.*2026*
109. **[Memory-Guided Hard Data Augmentation for Multimodal Named Entity Recognition](https://md.research.elamur.ai/free/en/papers/liu-2026-memory-guided-hard-data-augmentation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xinyu Liu, Kai fu, Yinghan Shi, et al.*2026*
110. **[MENTOR: Efficient Autoregressive Image Generation with Balanced Multimodal Control](https://md.research.elamur.ai/free/en/papers/zhao-2026-mentor-efficient-autoregressive-image-generation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Haozhe Zhao, Zefan Cai, Shuzheng Si, et al.*2026*
111. **[Multilingual-To-Multimodal (M2M): Unlocking New Languages with Monolingual Text](https://md.research.elamur.ai/free/en/papers/pasi-2026-multilingual-multimodal-m2m-unlocking-new/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Piyush Singh Pasi*2026*
112. **[Multimodal Large Language Models for Multi-Subject In-Context Image Generation](https://md.research.elamur.ai/free/en/papers/zhou-2026-multimodal-large-language-models-multi/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yucheng Zhou, Dubing Chen, Huan Zheng, et al.*2026*
113. **[MUSIA: Multilingual Story Illustration Corpus for Cross-Cultural Alignment and Generation](https://md.research.elamur.ai/free/en/papers/tewari-2026-musia-multilingual-story-illustration-corpus/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Krishna Tewari, Supriya Chanda, Nirmit Patil, et al.*2026*
114. **[PURE: Post-hoc Unlocking and REfinement for Discrete Diffusion Decoding](https://md.research.elamur.ai/free/en/papers/park-2026-pure-post-hoc-unlocking-refinement/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yangryeol Park, Kunhui Lee, Hanback Choi, et al.*2026*
115. **[SCMAPR: Self-Correcting Multi-Agent Prompt Refinement for Complex-Scenario Text-to-Video Generation](https://md.research.elamur.ai/free/en/papers/yang-2026-scmapr-self-correcting-multi-agent/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chengyi Yang, Pengzhen Li, Jiayin Qi, et al.*2026*
116. **[Sem-DPO: Mitigating Semantic Inconsistency in Preference Optimization for Prompt Engineering](https://md.research.elamur.ai/free/en/papers/mohamed-2026-sem-dpo-mitigating-semantic-inconsistency/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Anas Mohamed, Azal Ahmad Khan, Xinran Wang, et al.*2026*
117. **[Text-Guided Multi-Scale Frequency Representation Adaptation](https://md.research.elamur.ai/free/en/papers/yan-2026-text-guided-multi-scale-frequency/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Weicai Yan, Xinhua Ma, Wang Lin, et al.*2026*
118. **[Towards Unified Multimodal Large Language Models: A survey](https://md.research.elamur.ai/free/en/papers/ma-2026-unified-multimodal-large-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xu Ma, Yitian Zhang, Yun Fu*2026*
119. **[UniCM: A Unified Consistency Model For Efficient Multimodal Generation and Understanding](https://md.research.elamur.ai/free/en/papers/xu-2026-unicm-unified-consistency-model-efficient/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chenkai Xu, Xu Wang, Zhenyi Liao, et al.*2026*
120. **[Visual Self-Fulfilling Alignment: Shaping Safety-Oriented Personas via Threat-Related Images](https://md.research.elamur.ai/free/en/papers/yang-2026-visual-self-fulfilling-alignment-shaping/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qishun Yang, Shu Yang, Lijie Hu, et al.*2026*
121. [**From Foundation Intelligence to General Intelligence: Status and Prospects of Large-Model-Based GenAI and AGI (从基础智能到通用智能: 基于大模型的GenAI和AGI之现状与展望) Acta Automatica Sinica (自动化学报)**Qinghai Miao (缪青海), Xingxia Wang (王兴霞), Jing Yang (杨静), et al.*2024*](https://md.research.elamur.ai/free/en/papers/miao-2024-foundation-intelligence-general/)
122. [**Questions After ChatGPT: Opportunities and Challenges of Very Large Pre-trained Models (问答ChatGPT之后: 超大预训练模型的机遇和挑战) Acta Automatica Sinica (自动化学报)**Jingwei Lu (卢经纬), Chao Guo (郭超), Xingyuan Dai (戴星原), et al.*2023*](https://md.research.elamur.ai/free/en/papers/lu-2023-questions-after-chatgpt/)

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1,140 more papers of our library cite it; they have no page here yet.

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