mlsanityData to 14 Oct 2026

X-IQE: eXplainable Image Quality Evaluation for Text-to-Image Generation with Visual Large Language Models

arXiv 2305.10843 ↗First posted 18 May 2023cs.CV cs.AIcited by 28

On the map: Diffusion models → Image generation, text-to-image and editing

Abstract (arXiv)

This paper introduces a novel explainable image quality evaluation approach called X-IQE, which leverages visual large language models (LLMs) to evaluate text-to-image generation methods by generating textual explanations. X-IQE utilizes a hierarchical Chain of Thought (CoT) to enable MiniGPT-4 to produce self-consistent, unbiased texts that are highly correlated with human evaluation. It offers several advantages, including the ability to distinguish between real and generated images, evaluate text-image alignment, and assess image aesthetics without requiring model training or fine-tuning. X-IQE is more cost-effective and efficient compared to human evaluation, while significantly enhancing the transparency and explainability of deep image quality evaluation models. We validate the effectiveness of our method as a benchmark using images generated by prevalent diffusion models. X-IQE demonstrates similar performance to state-of-the-art (SOTA) evaluation methods on COCO Caption, while overcoming the limitations of previous evaluation models on DrawBench, particularly in handling ambiguous generation prompts and text recognition in generated images. Project website: https://github.com/Schuture/Benchmarking-Awesome-Diffusion-Models

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. Builds on 42 of its 58 references that are in the map. First posted on arXiv 18 May 2023.

Builds on

42 of 58 references.

Closest ancestors · 2

Same direction, newest first

  1. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding23 May
  2. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models20 Dec

Other references · 40

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2020Language Models are Few-Shot Learnerscited by 32,419
  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
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28 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 6; 2024: 14; 2025: 5; 2026: 3.

28 papers.

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  1. 2023GenEval: An Object-Focused Framework for Evaluating Text-to-Image Alignmentcited by 972
  2. 2023T2I-CompBench++: An Enhanced and Comprehensive Benchmark for Compositional Text-to-image Generationcited by 260
  3. 2023AGIQA-3K: An Open Database for AI-Generated Image Quality Assessmentcited by 142
  4. 2023Navigating Text-To-Image Customization: From LyCORIS Fine-Tuning to Model Evaluationcited by 109
  5. 2024Commonsense-T2I Challenge: Can Text-to-Image Generation Models Understand Commonsense?cited by 52
  6. 2024MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Modelscited by 33
  7. 2024LMM-VQA: Advancing Video Quality Assessment with Large Multimodal Modelscited by 28
  8. 2023Exploring the Naturalness of AI-Generated Imagescited by 22
  9. 2024Latent Watermark: Inject and Detect Watermarks in Latent Diffusion Spacecited by 19
  10. 2024SynArtifact: Classifying and Alleviating Artifacts in Synthetic Images via Vision-Language Modelcited by 15
  11. 2024MoLE: Enhancing Human-centric Text-to-image Diffusion via Mixture of Low-rank Expertscited by 15
  12. 2024FlashEval: Towards Fast and Accurate Evaluation of Text-to-image Diffusion Generative Modelscited by 11
16 more
  1. 2024XEdgeAI: A Human-centered Industrial Inspection Framework with Data-centric Explainable Edge AI Approachcited by 8
  2. 2026ImagenWorld: Stress-Testing Image Generation Models with Explainable Human Evaluation on Open-ended Real-World Taskscited by 7
  3. 2024DCA-Bench: A Benchmark for Dataset Curation Agentscited by 6
  4. 2023Benchmarking Sequential Visual Input Reasoning and Prediction in Multimodal Large Language Modelscited by 5
  5. 2025Reward-Agnostic Prompt Optimization for Text-to-Image Diffusion Modelscited by 5
  6. 2025RAG-IGBench: Innovative Evaluation for RAG-based Interleaved Generation in Open-domain Question Answeringcited by 4
  7. 2024Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunitiescited by 3
  8. 2025A High-Quality Dataset and Reliable Evaluation for Interleaved Image-Text Generationcited by 3
  9. 2025Calibrating MLLM-as-a-judge via Multimodal Bayesian Prompt Ensemblescited by 2
  10. 2026QualiRAG: Retrieval-Augmented Generation for Visual Quality Understandingcited by 2
  11. 2026ViPO: Visual Preference Optimization at Scalecited by 2
  12. 2024Interactive Visual Assessment for Text-to-Image Generation Modelscited by 1
  13. 2025Towards Evaluating Robustness of Prompt Adherence in Text to Image Modelscited by 1
  14. 2024ReFeR: Improving Evaluation and Reasoning through Hierarchy of Modelscited by 0
  15. 2024A New Chinese Landscape Paintings Generation Model based on Stable Diffusion using DreamBoothcited by 0
  16. 2024ABHINAW: A method for Automatic Evaluation of Typography within AI-Generated Imagescited by 0

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  1. 2024OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation
  2. 2024Automatic Evaluation for Text-to-image Generation: Task-decomposed Framework, Distilled Training, and Meta-evaluation Benchmark
  3. 2023VIEScore: Towards Explainable Metrics for Conditional Image Synthesis Evaluation
  4. 2024GenAI-Bench: Evaluating and Improving Compositional Text-to-Visual Generation
  5. 2024Image2Text2Image: A Novel Framework for Label-Free Evaluation of Image-to-Text Generation with Text-to-Image Diffusion Models
  6. 2023Depicting Beyond Scores: Advancing Image Quality Assessment through Multi-modal Language Models
  7. 2024A Comprehensive Study of Multimodal Large Language Models for Image Quality Assessment
  8. 2025OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation