Buddha statues are a part of human culture, especially of the Asia area, and they have been alongside human civilisation for more than 2,000 years. As history goes by, due to wars, natural disasters, and other reasons, the records that show the built years of Buddha statues went missing, which makes it an immense work for historians to estimate the built years. In this paper, we pursue the idea of building a neural network model that automatically estimates the built years of Buddha statues based only on their face images. Our model uses a loss function that consists of three terms: an MSE loss that provides the basis for built year estimation; a KL divergence-based loss that handles the samples with both an exact built year and a possible range of built years (e.g., dynasty or centuries) estimated by historians; finally a regularisation that utilises both labelled and unlabelled samples based on manifold assumption. By combining those three terms in the training process, we show that our method is able to estimate built years for given images with 37.5 years of a mean absolute error on the test set.
翻译:佛像是人类文化的一部分,特别是亚洲地区的佛像,它们与人类文明相伴已超过2000年。历史经过了2 000多年,由于战争、自然灾害和其他原因,表明佛像建成年数的记录丢失了,使历史学家对所建年数作出大量估计。在本文中,我们追求建立一个神经网络模型的想法,该模型仅根据面部图像自动估计佛像建成年数。我们的模型使用一个由三个术语组成的损失函数:MSE损失,为建筑的年份估计提供依据;KL差异损失,用历史学家估计的精确年数和可能的建筑年数(例如,元数或数世纪)来处理样品;最后,根据多种假设使用贴标签和未贴标签的样品的正规化。在培训过程中,我们把这三个术语结合起来,表明我们的方法能够估计所建年数,其中给出的图像有37.5年的绝对误差。