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Found 13 papers, 6 papers with code
The Average Mixing Kernel Signature
We introduce the Average Mixing Kernel Signature (AMKS), a novel signature for points on non-rigid three-dimensional shapes based on the average mixing kernel and continuous-time quantum walks.
Graph Generation via Spectral Diffusion
In this paper, we present GRASP, a novel graph generative model based on 1) the spectral decomposition of the graph Laplacian matrix and 2) a diffusion process.
GNN-LoFI: a Novel Graph Neural Network through Localized Feature-based Histogram Intersection
Graph neural networks are increasingly becoming the framework of choice for graph-based machine learning.
Deep Demosaicing for Polarimetric Filter Array Cameras
Polarisation Filter Array (PFA) cameras allow the analysis of light polarisation state in a simple and cost-effective manner.
Towards Exemplar-Free Continual Learning in Vision Transformers: an Account of Attention, Functional and Weight Regularization
In this paper, we investigate the continual learning of Vision Transformers (ViT) for the challenging exemplar-free scenario, with special focus on how to efficiently distill the knowledge of its crucial self-attention mechanism (SAM).
Graph Kernel Neural Networks
The convolution operator at the core of many modern neural architectures can effectively be seen as performing a dot product between an input matrix and a filter.
Smaller Is Better: An Analysis of Instance Quantity/Quality Trade-off in Rehearsal-based Continual Learning
The design of machines and algorithms capable of learning in a dynamically changing environment has become an increasingly topical problem with the increase of the size and heterogeneity of data available to learning systems.
Unsupervised semantic discovery through visual patterns detection
We propose a new fast fully unsupervised method to discover semantic patterns.
Parameter-Free Lens Distortion Calibration of Central Cameras
At the core of many Computer Vision applications stands the need to define a mathematical model describing the imaging process.
Partial Functional Correspondence
In this paper, we propose a method for computing partial functional correspondence between non-rigid shapes.
A Statistical Model of Riemannian Metric Variation for Deformable Shape Analysis
The analysis of deformable 3D shape is often cast in terms of the shape's intrinsic geometry due to its invariance to a wide range of non-rigid deformations.
Adopting an Unconstrained Ray Model in Light-Field Cameras for 3D Shape Reconstruction
This results in several drawbacks, ranging from the difficulties in feature detection, due to the reduced size of each microlens, to the need to adopt a model with a relatively small number of parameters.
Can a Fully Unconstrained Imaging Model Be Applied Effectively to Central Cameras?
Traditional camera models are often the result of a compromise between the ability to account for non-linearities in the image formation model and the need for a feasible number of degrees of freedom in the estimation process.
