Few-shot bronze vessel classification via siamese fourier networks

Abstract

Exploring ancient Chinese artifacts is crucial for analyzing East Asian technological development, with bronze vessel being the critical element. Bronze vessels, typically featuring intricate carvings, hold historical significance and provide valuable insights into past civilizations. However, identifying bronze patterns can be challenging for human vision, and most RGB-domain methods fail to capture periodic designs. Addressing these issues, we propose the Siamese Fourier Networks (SFN), a parallel network model designed for few-shot regular pattern classification. The Siamese network can differentiate between intricate shapes, while Fourier features enable the extraction of regular textures. To optimize parallel networks, we combine the BCE loss and focal contrastive loss, balancing positive and negative samples. Moreover, we introduce the Bronze Vessel Dataset, featuring 527 samples with diverse shapes and unbalanced distributions. Extensive experiments with advanced few-shot methods demonstrate the superiority of SFN and focal mechanism, significantly improving accuracy.