{"total":24,"rows":[{"id":"2410.15067","title":"A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trends","created":"2024-10-19","cited_by_count":60,"area":null},{"id":"2406.03070","title":"A-Bench: Are LMMs Masters at Evaluating AI-generated Images?","created":"2024-06-05","cited_by_count":39,"area":null},{"id":"2404.16687","title":"NTIRE 2024 Quality Assessment of AI-Generated Content Challenge","created":"2024-04-25","cited_by_count":20,"area":null},{"id":"2412.04508","title":"Video Quality Assessment: A Comprehensive Survey","created":"2024-12-04","cited_by_count":15,"area":null},{"id":"2412.11170","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","created":"2024-12-15","cited_by_count":8,"area":null},{"id":"2505.16314","title":"NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment","created":"2025-05-22","cited_by_count":8,"area":null},{"id":"2503.00811","title":"Evaluating and Predicting Distorted Human Body Parts for Generated Images","created":"2025-03-02","cited_by_count":5,"area":null},{"id":"2511.18825","title":"Q-Save: Towards Scoring and Attribution for Generated Video Evaluation","created":"2025-11-24","cited_by_count":4,"area":null},{"id":"2410.08534","title":"Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunities","created":"2024-10-11","cited_by_count":3,"area":null},{"id":"2502.15167","title":"M3-AGIQA: Multimodal, Multi-Round, Multi-Aspect AI-Generated Image Quality Assessment","created":"2025-02-21","cited_by_count":2,"area":null},{"id":"2512.12220","title":"TechImage-Bench: Rubric-Based Evaluation for Technical Image Generation","created":"2025-12-13","cited_by_count":2,"area":null},{"id":"2412.17632","title":"D-Judge: How Far Are We? Assessing the Discrepancies Between AI-synthesized and Natural Images through Multimodal Guidance","created":"2024-12-23","cited_by_count":1,"area":null},{"id":"2505.19233","title":"RAISE: Realness Assessment for Image Synthesis and Evaluation","created":"2025-05-25","cited_by_count":1,"area":null},{"id":"2507.23343","title":"Who is a Better Talker: Subjective and Objective Quality Assessment for AI-Generated Talking Heads","created":"2025-07-31","cited_by_count":1,"area":null},{"id":"2511.18055","title":"IE-Critic-R1: Advancing the Explanatory Measurement of Text-Driven Image Editing for Human Perception Alignment","created":"2025-11-22","cited_by_count":1,"area":null},{"id":"2602.03558","title":"ELIQ: A Label-Free Framework for Quality Assessment of Evolving AI-Generated Images","created":"2026-02-03","cited_by_count":1,"area":null},{"id":"2404.18409","title":"PKU-AIGIQA-4K: A Perceptual Quality Assessment Database for Both Text-to-Image and Image-to-Image AI-Generated Images","created":"2024-04-29","cited_by_count":0,"area":null},{"id":"2505.17619","title":"CAS-IQA: Teaching Vision-Language Models for Synthetic Angiography Quality Assessment","created":"2025-05-23","cited_by_count":0,"area":null},{"id":"2511.16908","title":"Q-REAL: Towards Realism and Plausibility Evaluation for AI-Generated Content","created":"2025-11-21","cited_by_count":0,"area":null},{"id":"2512.05098","title":"Beyond Pixels: Benchmarking and Reward-Based Assessing Framework for Visual Spatial Aesthetics","created":"2025-12-04","cited_by_count":0,"area":null},{"id":"2512.05209","title":"DEAR: Dataset for Evaluating the Aesthetics of Rendering","created":"2025-12-04","cited_by_count":0,"area":null},{"id":"2605.20731","title":"TASTE: A Designer-Annotated Multi-Dimensional Preference Dataset for AI-Generated Graphic Design","created":"2026-05-20","cited_by_count":0,"area":null},{"id":"2607.05605","title":"Patch Knowledge Transfer for Efficient AI-Generated Image Quality Assessment","created":"2026-07-06","cited_by_count":0,"area":null},{"id":"2608.24372","title":"Bridging Adversarial and Collaborative Learning for AI-Generated Image Quality Assessment","created":"2026-08-25","cited_by_count":0,"area":null}],"next_cursor":null}