NeRF-based image augmentation enables accurate target-specific spacecraft pose estimators to be trained from only 25-400 real images without CAD models or large synthetic datasets.
arXiv preprint arXiv:2007.13003 (2020) 3
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FSAM integrates a frequency adapter into SAM with LoRA to extract domain-invariant high-frequency features and outperforms prior domain generalization methods on fundus and prostate datasets.
MaskGen improves domain generalization for biomedical image segmentation by using source intensities plus domain-stable foundation model representations with minimal added complexity.
Targeted data augmentations let single-sequence 3D spine segmentation models generalize to seven unseen CT and MRI datasets with 155% average Dice gain and almost no in-domain loss.
FGML-DG applies Feynman-inspired principles of concept simplification, memory recall, and error-focused retraining within a meta-learning setup to enhance domain generalization for medical image segmentation.
citing papers explorer
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CAD-Free Learning of Spacecraft Pose Estimators via NeRF-Based Augmentations
NeRF-based image augmentation enables accurate target-specific spacecraft pose estimators to be trained from only 25-400 real images without CAD models or large synthetic datasets.
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Frequency Adapter with SAM for Generalized Medical Image Segmentation
FSAM integrates a frequency adapter into SAM with LoRA to extract domain-invariant high-frequency features and outperforms prior domain generalization methods on fundus and prostate datasets.
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Why Invariance is Not Enough for Biomedical Domain Generalization and How to Fix It
MaskGen improves domain generalization for biomedical image segmentation by using source intensities plus domain-stable foundation model representations with minimal added complexity.
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One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation
Targeted data augmentations let single-sequence 3D spine segmentation models generalize to seven unseen CT and MRI datasets with 155% average Dice gain and almost no in-domain loss.
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FGML-DG: Feynman-Inspired Cognitive Science Paradigm for Cross-Domain Medical Image Segmentation
FGML-DG applies Feynman-inspired principles of concept simplification, memory recall, and error-focused retraining within a meta-learning setup to enhance domain generalization for medical image segmentation.