MDS-VQA: Model-Informed Data Selection for Video Quality Assessment

• URL: http://arxiv.org/abs/2603.11525v1
• Date: Thu, 12 Mar 2026 04:19:42 GMT
• Title: MDS-VQA: Model-Informed Data Selection for Video Quality Assessment
• Abstract summary: We describe MDS-VQA, a model-informed data selection mechanism for curating unlabeled videos.<n>Difficulty is estimated by a failure predictor trained with a ranking objective, and diversity is measured using deep semantic video features.<n> Experiments across multiple VQA datasets and models demonstrate that MDS-VQA identifies diverse, challenging samples.
• Score: 39.416519746709305
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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