[Submitted on 22 Sep 2026 (v1), last revised 5 Nov 2026 (this version, v2)]
Abstract:Image quality assessment predicts a mean opinion score (MOS), but for image aesthetics the mean hides the individual, so personalized assessment must start from the few images a new user rates. Fitting each person from scratch fails with ten ratings; networks fine-tuned per person carry over poorly to new kinds of images. We present Undertone-P, which learns from other raters a Gaussian prior over per-person regression weights on read-outs of frozen vision-language model (VLM) judges: the probability-weighted rating and the hidden state at the score token. Since people differ from the crowd in ways other raters reveal, a few ratings need only choose among those ways: a new user costs one closed-form solve, and no network is trained. On Ryu and Yanaka’s PARA benchmark, the prior alone reaches a Spearman correlation of .640, above the best published 10- and 100-rating results (.581 and .620); 10 and 100 of a person’s ratings raise it to .648 and .676. These ratings add most where tastes depart from the crowd: with 100 ratings, +.067 for the raters who agree least with it and +.206 on LAPIS artworks, against +.037 overall. Without labels, the judges’ expected scores predict PARA’s MOS at .835 (best published model we ran: .694). PARA-trained networks still lead under PARA’s original protocol, likely through a stronger end-to-end MOS model.
Submission history
From: Amit Jadhav [view email]
[v1] Tue, 22 Sep 2026 22:07:52 UTC (85 KB)
[v2] Thu, 5 Nov 2026 00:33:14 UTC (96 KB)
