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NR-IQA
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To avoid duplication and fragmentation, use the No-Reference Image Quality Assessment (NR-IQA) task.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
VisualQuality-R1: Reasoning-Induced Image Quality Assessment via Reinforcement Learning to Rank
Semantically-Aware Game Image Quality Assessment
Exploring Semantic Feature Discrimination for Perceptual Image Super-Resolution and Opinion-Unaware No-Reference Image Quality Assessment
Cross-Domain Underwater Image Enhancement Guided by No-Reference Image Quality Assessment: A Transfer Learning Approach
IQPFR: An Image Quality Prior for Blind Face Restoration and Beyond
Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions
Backdoor Attacks against No-Reference Image Quality Assessment Models via a Scalable Trigger
Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment
Visual question answering based evaluation metrics for text-to-image generation
Multi-task Feature Enhancement Network for No-Reference Image Quality Assessment
AIM 2024 Challenge on UHD Blind Photo Quality Assessment
Attention Down-Sampling Transformer, Relative Ranking and Self-Consistency for Blind Image Quality Assessment
A Deep-Learning-Based Label-free No-Reference Image Quality Assessment Metric: Application in Sodium MRI Denoising
MSLIQA: Enhancing Learning Representations for Image Quality Assessment through Multi-Scale Learning
Local Manifold Learning for No-Reference Image Quality Assessment
UHD-IQA Benchmark Database: Pushing the Boundaries of Blind Photo Quality Assessment
GenzIQA: Generalized Image Quality Assessment using Prompt-Guided Latent Diffusion Models
Rank-based No-reference Quality Assessment for Face Swapping
ATTIQA: Generalizable Image Quality Feature Extractor using Attribute-aware Pretraining
Cross-IQA: Unsupervised Learning for Image Quality Assessment
Image Quality Assessment With Compressed Sampling
Beyond Score Changes: Adversarial Attack on No-Reference Image Quality Assessment from Two Perspectives
When No-Reference Image Quality Models Meet MAP Estimation in Diffusion Latents
Black-box Adversarial Attacks Against Image Quality Assessment Models
Diffusion Model Based Visual Compensation Guidance and Visual Difference Analysis for No-Reference Image Quality Assessment
Exploring Vulnerabilities of No-Reference Image Quality Assessment Models: A Query-Based Black-Box Method
Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivity
Less is More: Learning Reference Knowledge Using No-Reference Image Quality Assessment
You Only Train Once: A Unified Framework for Both Full-Reference and No-Reference Image Quality Assessment
Interpretable Image Quality Assessment via CLIP with Multiple Antonym-Prompt Pairs
JNDMix: JND-Based Data Augmentation for No-reference Image Quality Assessment
StyleAM: Perception-Oriented Unsupervised Domain Adaption for Non-reference Image Quality Assessment
Subjective Quality Assessment for Images Generated by Computer Graphics
ER-IQA: Boosting Perceptual Quality Assessment Using External Reference Images
Perceptual Image Restoration with High-Quality Priori and Degradation Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.