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Found 8 papers, 8 papers with code
DG-TTA: Out-of-domain Medical Image Segmentation through Augmentation and Descriptor-driven Domain Generalization and Test-Time Adaptation
Domain-generalized pre-training on source data is used to obtain the best initial performance in the target domain.
Shape Matters: Detecting Vertebral Fractures Using Differentiable Point-Based Shape Decoding
In this study, we specifically explore the use of shape auto-encoders for vertebrae, taking advantage of advancements in automated multi-label segmentation and the availability of large datasets for unsupervised learning.
Unsupervised 3D registration through optimization-guided cyclical self-training
State-of-the-art deep learning-based registration methods employ three different learning strategies: supervised learning, which requires costly manual annotations, unsupervised learning, which heavily relies on hand-crafted similarity metrics designed by domain experts, or learning from synthetic data, which introduces a domain shift.
A denoised Mean Teacher for domain adaptive point cloud registration
Self-training with the Mean Teacher is an established approach to this problem but is impaired by the inherent noise of the pseudo labels from the teacher.
Chasing Clouds: Differentiable Volumetric Rasterisation of Point Clouds as a Highly Efficient and Accurate Loss for Large-Scale Deformable 3D Registration
Learning-based registration for large-scale 3D point clouds has been shown to improve robustness and accuracy compared to classical methods and can be trained without supervision for locally rigid problems.
Anatomy-guided domain adaptation for 3D in-bed human pose estimation
As a remedy, we present a novel domain adaptation method, adapting a model from a labeled source to a shifted unlabeled target domain.
Adapting the Mean Teacher for keypoint-based lung registration under geometric domain shifts
We build on a keypoint-based registration model, combining graph convolutions for geometric feature learning with loopy belief optimization, and propose to reduce the domain shift through self-ensembling.
Fusing Posture and Position Representations for Point Cloud-Based Hand Gesture Recognition
To induce the global and local stream to capture complementary position and posture features, we propose the use of different 3D learning architectures in both streams.
Ranked #2 on
Hand Gesture Recognition
on SHREC 2017
