Leveraging Model Soups to Classify Intangible Cultural Heritage Images from the Mekong Delta
The classification of Intangible Cultural Heritage (ICH) images in the Mekong Delta poses unique challenges due to limited annotated data, high visual similarity among classes, and domain heterogeneity. In such low-resource settings, conventional deep learning models often suffer from high variance or overfit to spurious correlations, leading to poor generalization. To address these limitations, we propose a robust framework that integrates the hybrid CoAtNet architecture with model soups, a lightweight weight-space ensembling technique that averages checkpoints from a single training trajectory without increasing inference cost. CoAtNet captures both local and global patterns through sta...
| Authors | Quoc-Khang Tran, Minh-Thien Nguyen, Nguyen-Khang Pham |
|---|---|
| Primary category | cs.CV |
| Published | 2 March 2026 |
| Last updated at source | 2 March 2026 |
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