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Found 7 papers, 1 papers with code
Generalizable Neural Fields as Partially Observed Neural Processes
Neural fields, which represent signals as a function parameterized by a neural network, are a promising alternative to traditional discrete vector or grid-based representations.
Hyperbolic Deep Learning in Computer Vision: A Survey
In this paper, we provide a categorization and in-depth overview of current literature on hyperbolic learning for computer vision.
NeMo: Learning 3D Neural Motion Fields From Multiple Video Instances of the Same Action
Empirically, we show that NeMo can recover 3D motion in sports using videos from the Penn Action dataset, where NeMo outperforms existing HMR methods in terms of 2D keypoint detection.
NeMo: 3D Neural Motion Fields from Multiple Video Instances of the Same Action
Empirically, we show that NeMo can recover 3D motion in sports using videos from the Penn Action dataset, where NeMo outperforms existing HMR methods in terms of 2D keypoint detection.
Staying in Shape: Learning Invariant Shape Representations using Contrastive Learning
Creating representations of shapes that are invari-ant to isometric or almost-isometric transforma-tions has long been an area of interest in shape anal-ysis, since enforcing invariance allows the learningof more effective and robust shape representations. Most existing invariant shape representations arehandcrafted, and previous work on learning shaperepresentations do not focus on producing invariantrepresentations.
Capturing implicit hierarchical structure in 3D biomedical images with self-supervised hyperbolic representations
To that end, we consider encoder-decoder architectures with a hyperbolic latent space, to explicitly capture hierarchical relationships present in subvolumes of the data.
Learning Hyperbolic Representations for Unsupervised 3D Segmentation
There exists a need for unsupervised 3D segmentation on complex volumetric data, particularly when annotation ability is limited or discovery of new categories is desired.
