mlsanityData to 7 Oct 2026

Exploring CLIP for Assessing the Look and Feel of Images

arXiv 2207.12396 ↗First posted 25 Jul 2022cs.CVcited by 974

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Cited by 322 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Measuring the perception of visual content is a long-standing problem in computer vision. Many mathematical models have been developed to evaluate the look or quality of an image. Despite the effectiveness of such tools in quantifying degradations such as noise and blurriness levels, such quantification is loosely coupled with human language. When it comes to more abstract perception about the feel of visual content, existing methods can only rely on supervised models that are explicitly trained with labeled data collected via laborious user study. In this paper, we go beyond the conventional paradigms by exploring the rich visual language prior encapsulated in Contrastive Language-Image Pre-training (CLIP) models for assessing both the quality perception (look) and abstract perception (feel) of images in a zero-shot manner. In particular, we discuss effective prompt designs and show an effective prompt pairing strategy to harness the prior. We also provide extensive experiments on controlled datasets and Image Quality Assessment (IQA) benchmarks. Our results show that CLIP captures meaningful priors that generalize well to different perceptual assessments. Code is avaliable at https://github.com/IceClear/CLIP-IQA.

Key facts. Cited by 974 papers in the map (data through 7 Oct 2026). Builds on 37 of its 74 references that are in the map. First posted on arXiv 25 Jul 2022.

Builds on

37 of 74 references.

References · 37

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  3. 2021CLIPScore: A Reference-free Evaluation Metric for Image Captioningcited by 2,388
  4. 2021Learning to Prompt for Vision-Language Modelscited by 2,269
  5. 2021MUSIQ: Multi-scale Image Quality Transformercited by 1,058
  6. 2021StyleCLIP: Text-Driven Manipulation of StyleGAN Imagerycited by 990
  7. 2021Open-vocabulary Object Detection via Vision and Language Knowledge Distillationcited by 903
  8. 2018Deep Retinex Decomposition for Low-Light Enhancementcited by 720
  9. 2021RegionCLIP: Region-based Language-Image Pretrainingcited by 612
  10. 2017The Perception-Distortion Tradeoffcited by 582
  11. 2021ClipCap: CLIP Prefix for Image Captioningcited by 557
  12. 2021DenseCLIP: Language-Guided Dense Prediction with Context-Aware Promptingcited by 530
25 more
  1. 2021Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesiscited by 464
  2. 2021Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modelingcited by 429
  3. 2019KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessmentcited by 351
  4. 2019Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Networkcited by 301
  5. 2016Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessmentcited by 224
  6. 2015Massive Online Crowdsourced Study of Subjective and Objective Picture Qualitycited by 208
  7. 2013Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Indexcited by 193
  8. 2020PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restorationcited by 177
  9. 2018PieAPP: Perceptual Image-Error Assessment through Pairwise Preferencecited by 158
  10. 2019RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolutioncited by 145
  11. 2020MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessmentcited by 144
  12. 2016Learning a No-Reference Quality Metric for Single-Image Super-Resolutioncited by 143
  13. 2016Photo Aesthetics Ranking Network with Attributes and Content Adaptationcited by 122
  14. 2017Natural Language Processing: State of The Art, Current Trends and Challengescited by 114
  15. 2021ArtEmis: Affective Language for Visual Artcited by 103
  16. 2018Real-world Noisy Image Denoising: A New Benchmarkcited by 74
  17. 2020Positional Encoding as Spatial Inductive Bias in GANscited by 69
  18. 2021Perceptual Image Quality Assessment with Transformerscited by 54
  19. 2022ProposalCLIP: Unsupervised Open-Category Object Proposal Generation via Exploiting CLIP Cuescited by 42
  20. 2018Contemplating Visual Emotions: Understanding and Overcoming Dataset Biascited by 41
  21. 2021Predict, Prevent, and Evaluate: Disentangled Text-Driven Image Manipulation Empowered by Pre-Trained Vision-Language Modelcited by 34
  22. 2021An Image is Worth More Than a Thousand Words: Towards Disentanglement in the Wildcited by 30
  23. 2017Building Emotional Machines: Recognizing Image Emotions through Deep Neural Networkscited by 14
  24. 2013Image color transfer to evoke different emotions based on color combinationscited by 7
  25. 2022Pseudo-labelling and Meta Reweighting Learning for Image Aesthetic Quality Assessmentcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 54; 2024: 236; 2025: 366; 2026: 315.

974 papers.

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  1. 2023Exploiting Diffusion Prior for Real-World Image Super-Resolutioncited by 486
  2. 2023ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shiftingcited by 360
  3. 2024OminiControl: Minimal and Universal Control for Diffusion Transformercited by 334
  4. 2022Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectivescited by 314
  5. 2023SeeSR: Towards Semantics-Aware Real-World Image Super-Resolutioncited by 311
  6. 2024Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wildcited by 281
  7. 2023Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspectivecited by 276
  8. 2023DiffBIR: Towards Blind Image Restoration with Generative Diffusion Priorcited by 255
  9. 2023Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Visioncited by 247
  10. 2024One-Step Effective Diffusion Network for Real-World Image Super-Resolutioncited by 243
  11. 2023SinSR: Diffusion-Based Image Super-Resolution in a Single Stepcited by 235
  12. 2023LucidDreamer: Domain-free Generation of 3D Gaussian Splatting Scenescited by 228
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  1. 2024WonderWorld: Interactive 3D Scene Generation from a Single Imagecited by 197
  2. 2024SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Modelscited by 161
  3. 2023Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Modelscited by 152
  4. 2025WorldScore: A Unified Evaluation Benchmark for World Generationcited by 143
  5. 2023AGIQA-3K: An Open Database for AI-Generated Image Quality Assessmentcited by 142
  6. 2025Teaching Large Language Models to Regress Accurate Image Quality Scores using Score Distributioncited by 139
  7. 2023Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolutioncited by 131
  8. 2023Iterative Prompt Learning for Unsupervised Backlit Image Enhancementcited by 107
  9. 2023Depicting Beyond Scores: Advancing Image Quality Assessment through Multi-modal Language Modelscited by 104
  10. 2023AutoDIR: Automatic All-in-One Image Restoration with Latent Diffusioncited by 98
  11. 2024Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA Approachcited by 93
  12. 2025Q-Insight: Understanding Image Quality via Visual Reinforcement Learningcited by 93
  13. 2023ARNIQA: Learning Distortion Manifold for Image Quality Assessmentcited by 87
  14. 2024Arbitrary-steps Image Super-resolution via Diffusion Inversioncited by 85
  15. 2024Text-IF: Leveraging Semantic Text Guidance for Degradation-Aware and Interactive Image Fusioncited by 83
  16. 2024TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-Resolutioncited by 83
  17. 2023LooseControl: Lifting ControlNet for Generalized Depth Conditioningcited by 68
  18. 2024AddSR: Accelerating Diffusion-based Blind Super-Resolution with Adversarial Diffusion Distillationcited by 66
  19. 2024Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Comparecited by 66
  20. 2023ITI-GEN: Inclusive Text-to-Image Generationcited by 64
  21. 2024Efficient Diffusion Model for Image Restoration by Residual Shiftingcited by 64
  22. 2024Degradation-Guided One-Step Image Super-Resolution with Diffusion Priorscited by 64
  23. 2023VILA: Learning Image Aesthetics from User Comments with Vision-Language Pretrainingcited by 63
  24. 2024Diffusion Models, Image Super-Resolution And Everything: A Surveycited by 63
  25. 2025SeedVR: Seeding Infinity in Diffusion Transformer Towards Generic Video Restorationcited by 62
  26. 2023PromptMagician: Interactive Prompt Engineering for Text-to-Image Creationcited by 58
  27. 2024A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trendscited by 58
  28. 2024Towards A Better Metric for Text-to-Video Generationcited by 50
  29. 2025DiT4SR: Taming Diffusion Transformer for Real-World Image Super-Resolutioncited by 50
  30. 2024FreeCustom: Tuning-Free Customized Image Generation for Multi-Concept Compositioncited by 49
  31. 2024Adversarial Diffusion Compression for Real-World Image Super-Resolutioncited by 49
  32. 2023Towards Explainable In-the-Wild Video Quality Assessment: A Database and a Language-Prompted Approachcited by 45
  33. 2025SeedVR2: One-Step Video Restoration via Diffusion Adversarial Post-Trainingcited by 45
  34. 2023RePrompt: Automatic Prompt Editing to Refine AI-Generative Art Towards Precise Expressionscited by 43
  35. 2023MetaCloak: Preventing Unauthorized Subject-driven Text-to-image Diffusion-based Synthesis via Meta-learningcited by 42
  36. 2024MatSynth: A Modern PBR Materials Datasetcited by 42
  37. 2024XPSR: Cross-modal Priors for Diffusion-based Image Super-Resolutioncited by 41
  38. 2024Score Distillation via Reparametrized DDIMcited by 41

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