mlsanityData to 7 Oct 2026

MinhwaNet: Faithful but Insufficient Object Grounding in Korean Folk Painting

arXiv 2606.09855 ↗First posted 26 May 2026cs.MM cs.CV cs.LGcited by 0

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Abstract (arXiv)

Korean folk painting (minhwa) is built from a small vocabulary of auspicious symbols, a tiger for protection, a pair of birds for marital harmony, a peony for wealth, that recur across many of its painted genres. This suggests an obvious computational approach, identify which symbols appear in a painting and read the genre from the inventory. Working with a public corpus that pairs whole paintings, eight-field bilingual curatorial captions, and a separate set of expert object crops, we find that this approach does not work. A model given only a list of which symbols a painting contains predicts the genre far worse than a model that fuses the image with the curatorial text, and forcing the genre representation to be object-grounded actively hurts accuracy. The visual evidence on which the genre prediction rests is nonetheless localized and inspectable. A leakage-safe object evidence map projected from a part-level detector is spatially faithful to where curators isolated symbolic objects and to a patch-based surrogate's own gradient saliency. We name this configuration a faithful-but-insufficient dissociation. The part-level explanation is honest about what the part-level model sees, yet the genre target turns on how symbols are arranged rather than on which ones appear. The same lens separates a content label that survives transfer to held-out source institutions, genre, from a style label that does not, era, a prediction we confirm on two further labels in the corpus. We release the multimodal system, a worked-example reading of one painting's evidence map against its catalogue, and a set of evaluation cautions that recur in long-tailed heritage collections.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 30 references that are in the map. First posted on arXiv 26 May 2026.

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References · 16

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4 more
  1. 2020Evaluating Weakly Supervised Object Localization Methods Rightcited by 104
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