mlsanityData to 7 Oct 2026

2AFC Prompting of Large Multimodal Models for Image Quality Assessment

arXiv 2402.01162 ↗First posted 2 Feb 2024cs.CV cs.AIcited by 20

On the map: Multimodal LLMs / vision-language models → Image editing and quality assessment

Abstract (arXiv)

While abundant research has been conducted on improving high-level visual understanding and reasoning capabilities of large multimodal models~(LMMs), their visual quality assessment~(IQA) ability has been relatively under-explored. Here we take initial steps towards this goal by employing the two-alternative forced choice~(2AFC) prompting, as 2AFC is widely regarded as the most reliable way of collecting human opinions of visual quality. Subsequently, the global quality score of each image estimated by a particular LMM can be efficiently aggregated using the maximum a posterior estimation. Meanwhile, we introduce three evaluation criteria: consistency, accuracy, and correlation, to provide comprehensive quantifications and deeper insights into the IQA capability of five LMMs. Extensive experiments show that existing LMMs exhibit remarkable IQA ability on coarse-grained quality comparison, but there is room for improvement on fine-grained quality discrimination. The proposed dataset sheds light on the future development of IQA models based on LMMs. The codes will be made publicly available at https://github.com/h4nwei/2AFC-LMMs.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Placed in Image editing and quality assessment, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 18 of its 41 references that are in the map. First posted on arXiv 2 Feb 2024.

Builds on

18 of 41 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Depicting Beyond Scores: Advancing Image Quality Assessment through Multi-modal Language Models14 Dec

Other references · 17

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  4. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  5. 2023mPLUG-Owl: Modularization Empowers Large Language Models with Multimodalitycited by 1,038
  6. 2022Exploring CLIP for Assessing the Look and Feel of Imagescited by 974
  7. 2017NIMA: Neural Image Assessmentcited by 387
  8. 2019KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessmentcited by 351
  9. 2019Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Networkcited by 301
  10. 2023InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Compositioncited by 297
  11. 2023Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspectivecited by 276
  12. 2023Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Visioncited by 247
5 more
  1. 2015Massive Online Crowdsourced Study of Subjective and Objective Picture Qualitycited by 208
  2. 2023Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Modelscited by 152
  3. 2023BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Modelscited by 41
  4. 2020DeepFL-IQA: Weak Supervision for Deep IQA Feature Learningcited by 29
  5. 2022Deep Feature Statistics Mapping for Generalized Screen Content Image Quality Assessmentcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 9; 2025: 9; 2026: 2.

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  1. 2025Teaching Large Language Models to Regress Accurate Image Quality Scores using Score Distributioncited by 139
  2. 2024Towards Open-ended Visual Quality Comparisoncited by 85
  3. 2024Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Comparecited by 66
  4. 2025SeRL: Self-Play Reinforcement Learning for Large Language Models with Limited Datacited by 50
  5. 2024A Comprehensive Study of Multimodal Large Language Models for Image Quality Assessmentcited by 43
  6. 2025Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM Reasoningcited by 43
  7. 2024Q-Bench-Video: Benchmarking the Video Quality Understanding of LMMscited by 39
  8. 2024Enhancing Descriptive Image Quality Assessment with A Large-scale Multi-modal Datasetcited by 38
  9. 2024FakeBench: Probing Explainable Fake Image Detection via Large Multimodal Modelscited by 36
  10. 2024LMM-VQA: Advancing Video Quality Assessment with Large Multimodal Modelscited by 28
  11. 2025AGHI-QA: A Subjective-Aligned Dataset and Metric for AI-Generated Human Imagescited by 13
  12. 2025Next Token Is Enough: Realistic Image Quality and Aesthetic Scoring with Multimodal Large Language Modelcited by 11
8 more
  1. 2025Q-Adapt: Adapting LMM for Visual Quality Assessment with Progressive Instruction Tuningcited by 6
  2. 2024Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunitiescited by 3
  3. 2025Diagnosing Corruption-Induced Reliability Failures in Vision-Language Modelscited by 3
  4. 2024Towards Unified Benchmark and Models for Multi-Modal Perceptual Metricscited by 2
  5. 2025CleanMAP: Distilling Multimodal LLMs for Confidence-Driven Crowdsourced HD Map Updatescited by 1
  6. 2026IQA-Spider: Unifying Multi-Granularity Image Quality Assessment with Reasoning, Grounding and Referringcited by 1
  7. 2025VQualA 2025 Challenge on Visual Quality Comparison for Large Multimodal Models: Methods and Resultscited by 0
  8. 2026JPEG AIC2026: A large-scale dataset for fine-grained assessment of image codingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Enhancing Image Quality Assessment Ability of LMMs via Retrieval-Augmented Generation
  2. 2025Revisiting MLLM Based Image Quality Assessment: Errors and Remedy
  3. 2026IQA-T1: Tool-based Visual Evidence Reasoning for Image Quality Assessment
  4. 2026Zoom-IQA: Image Quality Assessment with Reliable Region-Aware Reasoning
  5. 2026Tool-IQA: Augmenting Image Quality Assessment with Simple Tools
  6. 2025Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA
  7. 2025iDETEX: Empowering MLLMs for Intelligent DETailed EXplainable IQA
  8. 2024VisualCritic: Making LMMs Perceive Visual Quality Like Humans