3 citations · 7 across the 5 of their papers we have counts for
5 papers
Generalizable Neural Fields as Partially Observed Neural Processes
Jeffrey Gu, Kuan-Chieh Wang, Serena Yeung
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. Co…
Hyperbolic Deep Learning in Computer Vision: A Survey
Pascal Mettes, Mina Ghadimi Atigh, Martin Keller-Ressel +2
Deep representation learning is a ubiquitous part of modern computer vision. While Euclidean space has been the de facto standard manifold for learning visual representations, hype…
NeMo: 3D Neural Motion Fields from Multiple Video Instances of the Same Action
Kuan-Chieh Wang, Zhenzhen Weng, Maria Xenochristou +4
The task of reconstructing 3D human motion has wideranging applications. The gold standard Motion capture (MoCap) systems are accurate but inaccessible to the general public due to…
Staying in Shape: Learning Invariant Shape Representations using Contrastive Learning
Jeffrey Gu, Serena Yeung
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 invar…
Capturing implicit hierarchical structure in 3D biomedical images with self-supervised hyperbolic representations
Joy Hsu, Jeffrey Gu, Gong-Her Wu +2
We consider the task of representation learning for unsupervised segmentation of 3D voxel-grid biomedical images. We show that models that capture implicit hierarchical relationshi…