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Found 4 papers, 0 papers with code
Learning Transferable Object-Centric Diffeomorphic Transformations for Data Augmentation in Medical Image Segmentation
To this end, we propose a novel object-centric data augmentation model that is able to learn the shape variations for the objects of interest and augment the object in place without modifying the rest of the image.
T2FNorm: Extremely Simple Scaled Train-time Feature Normalization for OOD Detection
Indeed, the naive incorporation of feature normalization within neural networks does not guarantee substantial improvement in OOD detection performance.
Interpretable Modeling and Reduction of Unknown Errors in Mechanistic Operators
Prior knowledge about the imaging physics provides a mechanistic forward operator that plays an important role in image reconstruction, although myriad sources of possible errors in the operator could negatively impact the reconstruction solutions.
Semi-Supervised Learning for Eye Image Segmentation
Recent advances in appearance-based models have shown improved eye tracking performance in difficult scenarios like occlusion due to eyelashes, eyelids or camera placement, and environmental reflections on the cornea and glasses.
