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Leveraging Model Soups to Classify Intangible Cultural Heritage Images from the Mekong Delta
🤖AI Summary
Researchers developed a new AI framework combining CoAtNet architecture with model soups technique to classify Intangible Cultural Heritage images from the Mekong Delta. The approach achieved 72.36% accuracy on the ICH-17 dataset, outperforming traditional models like ResNet-50 and ViT by reducing variance and improving generalization in low-resource settings.
Key Takeaways
- →Model soups technique averages checkpoints from training to reduce variance without increasing inference costs.
- →CoAtNet architecture captures both local and global patterns through fusion of convolution and self-attention mechanisms.
- →The framework achieved state-of-the-art 72.36% top-1 accuracy on ICH-17 dataset with 7,406 images across 17 classes.
- →Model soups selects geometrically diverse checkpoints, providing better generalization than traditional ensemble methods.
- →The approach addresses key challenges in cultural heritage classification including limited data and high visual similarity between classes.
#machine-learning#computer-vision#cultural-heritage#ensemble-methods#coatnet#model-soups#classification#low-resource-learning
Read Original →via arXiv – CS AI
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