Visual-Language Model Knowledge Distillation Method for Image Quality Assessment
On the map: Multimodal LLMs / vision-language models → Image editing and quality assessment
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Abstract (arXiv)
Image Quality Assessment (IQA) is a core task in computer vision. Multimodal methods based on vision-language models, such as CLIP, have demonstrated exceptional generalization capabilities in IQA tasks. To address the issues of excessive parameter burden and insufficient ability to identify local distorted features in CLIP for IQA, this study proposes a visual-language model knowledge distillation method aimed at guiding the training of models with architectural advantages using CLIP's IQA knowledge. First, quality-graded prompt templates were designed to guide CLIP to output quality scores. Then, CLIP is fine-tuned to enhance its capabilities in IQA tasks. Finally, a modality-adaptive knowledge distillation strategy is proposed to achieve guidance from the CLIP teacher model to the student model. Our experiments were conducted on multiple IQA datasets, and the results show that the proposed method significantly reduces model complexity while outperforming existing IQA methods, demonstrating strong potential for practical deployment.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Image editing and quality assessment, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 19 of its 27 references that are in the map. First posted on arXiv 21 Jul 2025.
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19 of 27 references.
References · 19
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
- 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
- 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
- 2021MUSIQ: Multi-scale Image Quality Transformercited by 1,081
- 2022Exploring CLIP for Assessing the Look and Feel of Imagescited by 991
- 2021MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformercited by 642
- 2019KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessmentcited by 355
- 2019Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Networkcited by 302
- 2023Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspectivecited by 280
- 2021No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistencycited by 166
7 more
- 2020MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessmentcited by 144
- 2023Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wildcited by 97
- 2023CLIP-KD: An Empirical Study of CLIP Model Distillationcited by 87
- 2020Transformer for Image Quality Assessmentcited by 76
- 2023Data-Efficient Image Quality Assessment with Attention-Panel Decodercited by 49
- 2023Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivitycited by 8
- 2023Local Distortion Aware Efficient Transformer Adaptation for Image Quality Assessmentcited by 4
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- 2026Zoom-IQA: Image Quality Assessment with Reliable Region-Aware Reasoning
- 2024Few-Shot Image Quality Assessment via Adaptation of Vision-Language Models
- 2025Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA
- 2023CLIP Brings Better Features to Visual Aesthetics Learners
- 2026Tool-IQA: Augmenting Image Quality Assessment with Simple Tools
- 2022Content-Variant Reference Image Quality Assessment via Knowledge Distillation
- 2024A Comprehensive Study of Multimodal Large Language Models for Image Quality Assessment
- 2026IQA-T1: Tool-based Visual Evidence Reasoning for Image Quality Assessment