用于AI生成视频质量评估的多层级语义感知模型
Multilevel Semantic-Aware Model for AI-Generated Video Quality Assessment
AI总结：
针对AI生成视频质量评估难题，提出MSA-VQA多层级语义感知模型，通过CLIP提示词语义监督、交叉注意力及语义突变感知模块，在帧、片段和视频层级建模并取得SOTA性能。
摘要：
近年来，扩散模型的快速发展极大推动了AI生成视频在长度和一致性方面的进步，但评估AI生成视频仍具挑战性。以往方法通常聚焦于用户生成内容（UGC），很少有研究专门针对AI生成视频质量评估方法。在本工作中，我们提出MSA-VQA，这是一种用于AI生成视频质量评估的多层级语义感知模型，利用基于CLIP的语义监督和交叉注意力机制。我们的分层框架在帧、片段和视频三个层级分析视频内容。我们提出提示词语义监督模块，使用CLIP的文本编码器确保视频与条件提示词之间的语义一致性。此外，我们提出语义突变感知模块以捕捉帧间的细微变化。大量实验表明，我们的方法取得了最先进的结果。
英文摘要：
The rapid development of diffusion models has greatly advanced AI-generated videos in terms of length and consistency recently, yet assessing AI-generated videos still remains challenging. Previous approaches have often focused on User-Generated Content(UGC), but few have targeted AI-Generated Video Quality Assessment methods. In this work, we introduce MSA-VQA, a Multilevel Semantic-Aware Model for AI-Generated Video Quality Assessment, which leverages CLIP-based semantic supervision and cross-attention mechanisms. Our hierarchical framework analyzes video content at three levels: frame, segment, and video. We propose a Prompt Semantic Supervision Module using text encoder of CLIP to ensure semantic consistency between videos and conditional prompts. Additionally, we propose the Semantic Mutation-aware Module to capture subtle variations between frames. Extensive experiments demonstrate our method achieves state-of-the-art results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
