基于模型反馈筛选难样本，提升视频质量评估精度
MDS-VQA: Model-Informed Data Selection for Video Quality Assessment
- 用失败预测器识别模型难处理的视频片段
- 5%精选数据使相关指标提升，显著改善模型表现
- 适合需要高效标注的高质量视频评估研究者
基于学习的视频质量评估（VQA）发展迅速，但模型设计与数据集构建之间存在脱节。现有方法多在固定基准上迭代模型，或盲目收集新的人类标签，未系统针对现有模型弱点。本文提出MDS-VQA，一种模型驱动的数据选择机制，用于筛选对基础模型困难且内容多样化的未标注视频。通过排名目标训练的失败预测器估计难度，利用深度语义特征衡量多样性，以贪婪算法在有限标注预算下平衡二者。在多个VQA数据集和模型上的实验表明，MDS-VQA能有效识别出具有挑战性且多样化的样本，特别适用于主动微调。仅使用每领域5%的精选子集，微调后模型的均值SRCC从0.651提升至0.722，并获得最优gMAD排名，表明其具备强适应性与泛化能力。
原文摘要 · Abstract (English)
Learning-based video quality assessment (VQA) has advanced rapidly, yet progress is increasingly constrained by a disconnect between model design and dataset curation. Model-centric approaches often iterate on fixed benchmarks, while data-centric efforts collect new human labels without systematically targeting the weaknesses of existing VQA models. Here, we describe MDS-VQA, a model-informed data selection mechanism for curating unlabeled videos that are both difficult for the base VQA model and diverse in content. Difficulty is estimated by a failure predictor trained with a ranking objective, and diversity is measured using deep semantic video features, with a greedy procedure balancing the two under a constrained labeling budget. Experiments across multiple VQA datasets and models demonstrate that MDS-VQA identifies diverse, challenging samples that are particularly informative for active fine-tuning. With only a 5% selected subset per target domain, the fine-tuned model improves mean SRCC from 0.651 to 0.722 and achieves the top gMAD rank, indicating strong adaptation and generalization.
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