用深度学习复原考古碎片艺术风格，提升分类准确率。
Recognizing Artistic Style of Archaeological Image Fragments Using Deep Style Extrapolation
- 提出通用深度学习框架，基于碎片图像预测艺术风格
- 在不同风格与几何形态碎片上达当前最优分类效果
- 适合考古图像分析、文物数字化研究者使用
考古发掘获得的古代艺术品常因破碎和物理退化而难以辨识。同一遗址可能混杂来自不同时期或风格的多个文物碎片，每块仅含部分信息，导致基于视觉线索对碎片进行分类极具挑战，即便专业人士亦难判定。由于分类是机器学习模型的常见功能，现代神经网络架构可有效用于高效精准地识别碎片归属。本文提出一种通用深度学习框架，用于预测图像碎片的艺术风格，在不同风格与几何形态的碎片上均取得当前最佳表现。
原文摘要 · Abstract (English)
Ancient artworks obtained in archaeological excavations usually suffer from a certain degree of fragmentation and physical degradation. Often, fragments of multiple artifacts from different periods or artistic styles could be found on the same site. With each fragment containing only partial information about its source, and pieces from different objects being mixed, categorizing broken artifacts based on their visual cues could be a challenging task, even for professionals. As classification is a common function of many machine learning models, the power of modern architectures can be harnessed for efficient and accurate fragment classification. In this work, we present a generalized deep-learning framework for predicting the artistic style of image fragments, achieving state-of-the-art results for pieces with varying styles and geometries.
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