Built Year Prediction from Buddha Face with Heterogeneous Labels

Abstract

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.

References in corpus (12)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• A Simple Framework for Contrastive Learning of Visual Representations
• FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
• Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
• MixMatch: A Holistic Approach to Semi-Supervised Learning
• RetinaFace: Single-stage Dense Face Localisation in the Wild
• Context-Aware Embeddings for Automatic Art Analysis
• Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance
• Partial FC: Training 10 Million Identities on a Single Machine
• Minimax-optimal semi-supervised regression on unknown manifolds
• Learning to Impute: A General Framework for Semi-supervised Learning
• Solving weakly supervised regression problem using low-rank manifold regularization