AI Can Now Name the Artist Behind Broken Ancient Frescoes

Fragment-aware AI for fresco-fragment style classification: normally, artistic style recognition is studied on complete artworks, where models can exploit global composition, spatial organisation, and iconographic structure. In archaeological settings, though, artworks often survive only as fragmented remains, forcing recognition from incomplete, irregular, and context-limited visual evidence. A team built a progressive transformer-based framework starting from a ViT-B/16 baseline, adding foreground-guided masking to suppress background-only tokens, inpainting-based geometric regularisation to align irregular fragment supports with the ViT patch grid, and a supervised contrastive objective operating on predictive distributions through a Kullback-Leibler similarity, combined with a deliberately simple learnable logit ensemble. On CLEOPATRA and POMPAAF, fragment-aware modelling improved over the standard ViT baseline, with the ensemble raising accuracy from 0.604 to 0.656 and macro-F1 from 0.596 to 0.648 on CLEOPATRA, and outperforming the best single branch in four of six fragmentation settings on POMPAAF. A more complex graph-fusion variant matched the simple ensemble on POMPAAF and offered only a small, dataset-specific gain on CLEOPATRA, which the authors say does not justify its added complexity. Beyond the empirical gains, the contribution is twofold: a distribution-level contrastive objective that consistently sharpens single-branch recognition, and an interpretability analysis verifying the models exploit genuine painted evidence while quantifying that the inpainting-based branch draws part of its attribution from the synthesised surround.