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arXiv Computer Vision By Ziqing Zhang, Xiao Liu, Kai Liu, Jianze Li, Weihang Zhang, Linghe Kong, Yulun Zhang

Discrete Annotation, Continuous Preference: Rethinking Supervision for Accurate and Generalizable Aesthetic Image Cropping

Read the original on arXiv Computer Vision →

The paper introduces a new approach to aesthetic image cropping by modeling human preference as a continuous, multi-peaked field rather than relying on discrete, grid‑based annotations. It presents the Continuous Preference Field (CPF) that reconstructs a dense preference landscape from sparse labels, and uses this to train a VLM‑based cropping model (CPIC) that achieves state‑of‑the‑art accuracy and strong out‑of‑domain generalization. Additionally, the authors propose CPICD, a recalibrated benchmark that corrects grid‑bound artifacts in existing datasets, providing a more reliable evaluation framework.

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 Computer Vision.

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