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arXiv AI By Chuanzhi Xu, Chengkun Yue, Haolan Guo, Xuanhua Yin, Boyu Wei, Zihan Deng, Qiang Qu, Weidong Cai

MetaRefine: Score Refinement with Multi-Level Features for Image Assessment

Read the original on arXiv AI →

MetaRefine is a lightweight, metadata‑aware score refinement architecture that uses original image geometry, multi‑level visual features, and a frozen model’s initial score to predict corrections. It improves correlation metrics (PLCC and SRCC) and reduces error metrics (MAE and RMSE) across 100 configurations on 21 datasets, with average gains of 7.55% and 7.10% and reductions of 23.27% and 21.93%, respectively. The method adds only a median 1.11% parameter overhead and can enhance state‑of‑the‑art assessment models on multiple benchmarks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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