MDS-VQA：面向视频质量评估的模型感知数据选择
MDS-VQA: Model-Informed Data Selection for Video Quality Assessment
提出一种模型感知的数据选择机制，通过失败预测器和多样性度量在有限标注预算下挑选难且多样的视频样本，可直接用于视觉任务中的主动微调与数据高效适配。
原文依据 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.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论基于学习的视频质量评估（VQA）已取得快速进展，但其进一步发展日益受到模型设计与数据集构建之间脱节的制约。以模型为中心的方法通常在固定基准上迭代，而以数据为中心的工作则收集新的人工标注，却没有系统地针对现有VQA模型的弱点。本文提出MDS-VQA，一种模型感知的数据选择机制，用于筛选对基础VQA模型而言既困难又内容多样的无标注视频。难度通过以排序目标训练的失败预测器来估计，多样性则使用深度语义视频特征来衡量，并在有限的标注预算下通过贪心过程平衡两者。在多个VQA数据集和模型上的实验表明，MDS-VQA能够识别出多样且具有挑战性的样本，这些样本对主动微调尤其具有信息量。每个目标域仅使用5%的选定子集，微调后的模型便将平均SRCC从0.651提升至0.722，并取得最佳的gMAD排名，表明其具有强大的适应性和泛化能力。
英文原文摘要 1109 字符
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.