AI RESEARCH
MDS-VQA: Model-Informed Data Selection for Video Quality Assessment
arXiv CS.CV
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ArXi:2603.11525v1 Announce Type: new 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.