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ARXIV:2603.02181 · IMAGE CLASSIFICATION · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE
ARXIV:2603.02181IMAGE CLASSIFICATIONSUBMITTED 19 MAR · 18:48 UTCFRESHNESS STALEarXiv
Develop a robust image classification tool for intangible cultural heritage images using model soups for enhanced generalization on low-resource datasets.
Opportunity summary
Pain Develop a robust image classification tool for intangible cultural heritage images using model soups for enhanced generalization on low-resource datasets.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
Develop a robust image classification tool for intangible cultural heritage images using model soups for enhanced generalization on low-resource datasets. In such low-resource settings, conventional deep learning models often suffer from high variance or…
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…
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Our findings show that model soups reduces variance by stabilizing predictions across diverse model snapshots, while introducing minimal additional bias.
Image Classification moved forward this cycle; last verified April 2026. Public score 6.0/10.
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Develop a robust image classification tool for intangible cultural heritage images using model soups for enhanced generalization on low-resource datasets.
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10.48550/arXiv.2603.02181Develop a robust image classification tool for intangible cultural heritage images using model soups for enhanced generalization on low-resource datasets.
Abstract
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 stage-wise fusion of convolution and self-attention. We apply two ensembling strategies - greedy and uniform soup - to selectively combine diverse checkpoints into a final model. Beyond performance improvements, we analyze the ensembling effect through the lens of bias-variance decomposition. Our findings show that model soups reduces variance by stabilizing predictions across diverse model snapshots, while introducing minimal additional bias. Furthermore, using cross-entropy-based distance metrics and Multidimensional Scaling (MDS), we show that model soups selects geometrically diverse checkpoints, unlike Soft Voting, which blends redundant models centered in output space. Evaluated on the ICH-17 dataset (7,406 images across 17 classes), our approach achieves state-of-the-art results with 72.36% top-1 accuracy and 69.28% macro F1-score, outperforming strong baselines including ResNet-50, DenseNet-121, and ViT. These results underscore that diversity-aware checkpoint averaging provides a principled and efficient way to reduce variance and enhance generalization in culturally rich, data-scarce classification tasks.
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PROBLEM
Develop a robust image classification tool for intangible cultural heritage images using model soups for enhanced generalization on low-resource datasets. In such low-resource settings, conventional deep learning models often suffer from high variance or overfit to spurious corr...
METHOD
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 suffe...
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Our findings show that model soups reduces variance by stabilizing predictions across diverse model snapshots, while introducing minimal additional bias.
WHY NOW
Image Classification moved forward this cycle; last verified April 2026. Public score 6.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Develop a robust image classification tool for intangible cultural heritage images using model soups for enhanced generalization on low-resource datasets. In such low-resource settings, conventional deep learning models often suffer from high variance or overfit to spurious correlations, leading to poor generalization.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Our findings show that model soups reduces variance by stabilizing predictions across diverse model snapshots, while introducing minimal additional bias.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Image Classification moved forward this cycle; last verified April 2026. Public score 6.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Develop a robust image classification tool for intangible cultural heritage images using model soups for enhanced generalization on low-resource datasets.
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