Q-Save: Towards Scoring and Attribution for Generated Video Evaluation

arXiv:2511.18825 · cs.CV · Submitted 2025-11-24 · Read on arXiv

cs.CV

Submitted: 2025-11-24

Updated: 2026-10-12

Terminology

• Accuracy — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Chain-of-Thought (CoT) — 1× in this paper · explained in 4 episodes
• Core — 1× in this paper · explained in 2 episodes
• Evaluation Dimensions — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• GRPO — 1× in this paper · explained in 6 episodes
• Supervised Fine-Tuning — 1× in this paper
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes

Sources

• Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
• A Note on the Inception Score
• Microsoft COCO Captions: Data Collection and Evaluation Server
• Exploring the Naturalness of AI-Generated Images
• GAIA: Rethinking Action Quality Assessment for AI-Generated Videos
• SimpleVQA: Multimodal Factuality Evaluation for Multimodal Large Language Models
• Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation
• Video-R1: Reinforcing Video Reasoning in MLLMs
• VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation
• VideoScore2: Think before You Score in Generative Video Evaluation
• Prometheus: Inducing Fine-grained Evaluation Capability in Language Models
• HunyuanVideo: A Systematic Framework For Large Video Generative Models
• Subjective-Aligned Dataset and Metric for Text-to-Video Quality Assessment
• VIEScore: Towards Explainable Metrics for Conditional Image Synthesis Evaluation
• GenAI-Bench: Evaluating and Improving Compositional Text-to-Visual Generation
• AIGIQA-20K: A Large Database for AI-Generated Image Quality Assessment
• Q-Insight: Understanding Image Quality via Visual Reinforcement Learning
• AGIQA-3K: An Open Database for AI-Generated Image Quality Assessment
• Evaluating Text-to-Visual Generation with Image-to-Text Generation
• Improving Video Generation with Human Feedback

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