VersusQ: Pairwise Margin Reasoning for Generalizable Video Quality Assessment

ArXi:2605.21130v1 Announce Type: new Large Multimodal Models (LMMs) have shown promise for video quality assessment, but most methods still predict an absolute score for each video. Such pointwise supervision often mixes perceptual quality with dataset-specific calibration, including annotation protocols, rating habits, and score distributions. As a result, the learned scoring rule may work well within a benchmark but transfer poorly across unseen domains.