FEDSNet improves few-shot fine-grained image classification by fusing spatial texture and frequency-based structural subspaces to reduce noise overfitting.
Feature complementation architecture for visual place recognition[J].arXiv preprint
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GloResNet, a ResNet-10-based lightweight 3D CNN pretrained on MedicalNet with global manifold mapping for topology preservation, achieves 75.18% average accuracy (peak 81.82%) in 5-fold cross-validation for preterm brain injury prediction.
citing papers explorer
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Frequency-Enhanced Dual-Subspace Networks for Few-Shot Fine-Grained Image Classification
FEDSNet improves few-shot fine-grained image classification by fusing spatial texture and frequency-based structural subspaces to reduce noise overfitting.
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GloResNet: A lightweight 3D CNN with global topological features for preterm brain injury prediction
GloResNet, a ResNet-10-based lightweight 3D CNN pretrained on MedicalNet with global manifold mapping for topology preservation, achieves 75.18% average accuracy (peak 81.82%) in 5-fold cross-validation for preterm brain injury prediction.