GeoMeld provides a large-scale aligned multimodal remote sensing dataset with verified semantic captions and a joint pretraining method that improves downstream transfer and cross-sensor robustness in foundation models.
Con- vnext v2: Co-designing and scaling convnets with masked autoencoders
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
LoRA fine-tuning delivers better GI disease classification accuracy than full end-to-end fine-tuning while using far fewer parameters.
citing papers explorer
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GeoMeld: Toward Semantically Grounded Foundation Models for Remote Sensing
GeoMeld provides a large-scale aligned multimodal remote sensing dataset with verified semantic captions and a joint pretraining method that improves downstream transfer and cross-sensor robustness in foundation models.
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Parameter Efficient Fine-tuning for Domain-specific Gastrointestinal Disease Recognition
LoRA fine-tuning delivers better GI disease classification accuracy than full end-to-end fine-tuning while using far fewer parameters.