# Quality Assessment in the Era of Large Models: A Survey

arXiv: https://arxiv.org/abs/2409.00031 · HTML: https://mlsanity.com/paper/2409.00031/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 142 of its 261 references that are in the map.
- First posted on arXiv 17 Aug 2024.

## Builds on (most cited first, 30 of 142)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://mlsanity.com/paper/2308.12966/) (2023)
- [MMBench: Is Your Multi-modal Model an All-around Player?](https://mlsanity.com/paper/2307.06281/) (2023)
- [MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://mlsanity.com/paper/2311.16502/) (2023)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [VBench: Comprehensive Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2311.17982/) (2023)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts](https://mlsanity.com/paper/2310.02255/) (2023)
- [Microsoft COCO Captions: Data Collection and Evaluation Server](https://mlsanity.com/paper/1504.00325/) (2015)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis](https://mlsanity.com/paper/2405.21075/) (2024)
- [Scaling Autoregressive Models for Content-Rich Text-to-Image Generation](https://mlsanity.com/paper/2206.10789/) (2022)
- [MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities](https://mlsanity.com/paper/2308.02490/) (2023)
- [Towards Accurate Generative Models of Video: A New Metric & Challenges](https://mlsanity.com/paper/1812.01717/) (2018)
- [OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge](https://mlsanity.com/paper/1906.00067/) (2019)
- [MVBench: A Comprehensive Multi-modal Video Understanding Benchmark](https://mlsanity.com/paper/2311.17005/) (2023)
- [Kosmos-2: Grounding Multimodal Large Language Models to the World](https://mlsanity.com/paper/2306.14824/) (2023)

## Built on by (most cited first, 26 of 26)

- [Teaching Large Language Models to Regress Accurate Image Quality Scores using Score Distribution](https://mlsanity.com/paper/2501.11561/) (2025)
- [VQA2: Visual Question Answering for Video Quality Assessment](https://mlsanity.com/paper/2411.03795/) (2024)
- [MEMO-Bench: A Multiple Benchmark for Text-to-Image and Multimodal Large Language Models on Human Emotion Analysis](https://mlsanity.com/paper/2411.11235/) (2024)
- [3DGCQA: A Quality Assessment Database for 3D AI-Generated Contents](https://mlsanity.com/paper/2409.07236/) (2024)
- [UVE: Are MLLMs Unified Evaluators for AI-Generated Videos?](https://mlsanity.com/paper/2503.09949/) (2025)
- [NTIRE 2025 XGC Quality Assessment Challenge: Methods and Results](https://mlsanity.com/paper/2506.02875/) (2025)
- [Perceptual Visual Quality Assessment: Principles, Methods, and Future Directions](https://mlsanity.com/paper/2503.00625/) (2025)
- [Gamma: Toward Generic Image Assessment with Mixture of Assessment Experts](https://mlsanity.com/paper/2503.06678/) (2025)
- [Q-Save: Towards Scoring and Attribution for Generated Video Evaluation](https://mlsanity.com/paper/2511.18825/) (2025)
- [Looking Beyond the Obvious: A Survey on Abstract Concept Recognition for Video Understanding](https://mlsanity.com/paper/2508.20765/) (2025)
- [On the Adversarial Robustness of Multimodal LLM Judges](https://mlsanity.com/paper/2606.15608/) (2026)
- [OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions](https://mlsanity.com/paper/2504.13524/) (2025)
- [Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models](https://mlsanity.com/paper/2506.20832/) (2025)
- [Segmenting and Understanding: Region-aware Semantic Attention for Fine-grained Image Quality Assessment with Large Language Models](https://mlsanity.com/paper/2508.07818/) (2025)
- [Enhancing Diffusion-based Restoration Models via Difficulty-Adaptive Reinforcement Learning with IQA Reward](https://mlsanity.com/paper/2511.01645/) (2025)
- [Scaling Audio-Visual Quality Assessment Dataset via Crowdsourcing](https://mlsanity.com/paper/2602.22659/) (2026)
- [Who is a Better Talker: Subjective and Objective Quality Assessment for AI-Generated Talking Heads](https://mlsanity.com/paper/2507.23343/) (2025)
- [OmniQuality-R: Advancing Reward Models Through All-Encompassing Quality Assessment](https://mlsanity.com/paper/2510.10609/) (2025)
- [AU-IQA: A Benchmark Dataset for Perceptual Quality Assessment of AI-Enhanced User-Generated Content](https://mlsanity.com/paper/2508.05016/) (2025)
- [Predictive Quality Assessment for Mobile Secure Graphics](https://mlsanity.com/paper/2509.20028/) (2025)
- [Cost Savings from Automatic Quality Assessment of Generated Images](https://mlsanity.com/paper/2510.16179/) (2025)
- [Enhancing Image Quality Assessment Ability of LMMs via Retrieval-Augmented Generation](https://mlsanity.com/paper/2601.08311/) (2026)
- [A3: Towards Advertising Aesthetic Assessment](https://mlsanity.com/paper/2603.24037/) (2026)
- [Non-identifiability of Explanations from Model Behavior in Deep Networks of Image Authenticity Judgments](https://mlsanity.com/paper/2604.07254/) (2026)
- [SpatialQ: Understanding 3D Gaussian Splatting Scene Quality via Visual-based MLLM](https://mlsanity.com/paper/2607.26595/) (2026)
- [PCQA-R1: Advancing Generalized 3D Point Cloud Quality Assessment with Reinforcement Learning](https://mlsanity.com/paper/2608.18627/) (2026)

## Abstract (arXiv)

Quality assessment, which evaluates the visual quality level of multimedia experiences, has garnered significant attention from researchers and has evolved substantially through dedicated efforts. Before the advent of large models, quality assessment typically relied on small expert models tailored for specific tasks. While these smaller models are effective at handling their designated tasks and predicting quality levels, they often lack explainability and robustness. With the advancement of large models, which align more closely with human cognitive and perceptual processes, many researchers are now leveraging the prior knowledge embedded in these large models for quality assessment tasks. This emergence of quality assessment within the context of large models motivates us to provide a comprehensive review focusing on two key aspects: 1) the assessment of large models, and 2) the role of large models in assessment tasks. We begin by reflecting on the historical development of quality assessment. Subsequently, we move to detailed discussions of related works concerning quality assessment in the era of large models. Finally, we offer insights into the future progression and potential pathways for quality assessment in this new era. We hope this survey will enable a rapid understanding of the development of quality assessment in the era of large models and inspire further advancements in the field.