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Found 7 papers, 4 papers with code
Learning to Augment: Hallucinating Data for Domain Generalized Segmentation
Domain generalized semantic segmentation (DGSS) is an essential but highly challenging task, in which the model is trained only on source data and any target data is not available.
O$n$ Learning Deep O($n$)-Equivariant Hyperspheres
In this paper, we utilize hyperspheres and regular $n$-simplexes and propose an approach to learning deep features equivariant under the transformations of $n$D reflections and rotations, encompassed by the powerful group of O$(n)$.
TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud Analysis
In our approach, we perform TetraTransform--an equivariant embedding of the 3D input into 4D, constructed from the steerable neurons--and extract deeper O(3)-equivariant features using vector neurons.
Fully Steerable 3D Spherical Neurons
Emerging from low-level vision theory, steerable filters found their counterpart in prior work on steerable convolutional neural networks equivariant to rigid transformations.
Steerable 3D Spherical Neurons
In our work, we propose a steerable feed-forward learning-based approach that consists of neurons with spherical decision surfaces and operates on point clouds.
Embed Me If You Can: A Geometric Perceptron
Our extension of the MLHP model, the multilayer geometric perceptron (MLGP), and its respective layer units, i. e., geometric neurons, are consistent with the 3D geometry and provide a geometric handle of the learned coefficients.
A High-Performance CNN Method for Offline Handwritten Chinese Character Recognition and Visualization
Recent researches introduced fast, compact and efficient convolutional neural networks (CNNs) for offline handwritten Chinese character recognition (HCCR).
