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Found 18 papers, 7 papers with code
A distribution-dependent Mumford-Shah model for unsupervised hyperspectral image segmentation
We equipped the MS functional with a novel robust distribution-dependent indicator function designed to handle the characteristic challenges of hyperspectral data.
E-CIR: Event-Enhanced Continuous Intensity Recovery
A camera begins to sense light the moment we press the shutter button.
Learning Optimal Control with Stochastic Models of Hamiltonian Dynamics
Optimal control problems can be solved by first applying the Pontryagin maximum principle, followed by computing a solution of the corresponding unconstrained Hamiltonian dynamical system.
Recursive Self-Improvement for Camera Image and Signal Processing Pipeline
Current camera image and signal processing pipelines (ISPs), including deep trained versions, tend to apply a single filter that is uniformly applied to the entire image.
Robust Learning of Physics Informed Neural Networks
Physics-informed Neural Networks (PINNs) have been shown to be effective in solving partial differential equations by capturing the physics induced constraints as a part of the training loss function.
Scene Synthesis via Uncertainty-Driven Attribute Synchronization
This task is challenging because 3D scenes exhibit diverse patterns, ranging from continuous ones, such as object sizes and the relative poses between pairs of shapes, to discrete patterns, such as occurrence and co-occurrence of objects with symmetrical relationships.
ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generators
Our approach builds on an approximation of the as-rigid-as possible (or ARAP) deformation energy.
Deep Predictive Learning of Carotid Stenosis Severity
Carotid artery stenosis is the narrowing of carotid arteries, which supplies blood to the neck and head.
Invariance-based Multi-Clustering of Latent Space Embeddings for Equivariant Learning
Variational Autoencoders (VAEs) have been shown to be remarkably effective in recovering model latent spaces for several computer vision tasks.
Learning Transferable 3D Adversarial Cloaks for Deep Trained Detectors
This paper presents a novel patch-based adversarial attack pipeline that trains adversarial patches on 3D human meshes.
Learning Deep Latent Subspaces for Image Denoising
We compare our results to a conventional one-encoder-one-decoder architecture.
Can 3D Adversarial Logos Cloak Humans?
Contrary to the traditional adversarial patch, this new form of attack is mapped into the 3D object world and back-propagates to the 2D image domain through differentiable rendering.
Stein Variational Gradient Descent With Matrix-Valued Kernels
Stein variational gradient descent (SVGD) is a particle-based inference algorithm that leverages gradient information for efficient approximate inference.
Blind Hyperspectral-Multispectral Image Fusion via Graph Laplacian Regularization
Fusing a low-resolution hyperspectral image (HSI) and a high-resolution multispectral image (MSI) of the same scene leads to a super-resolution image (SRI), which is information rich spatially and spectrally.
SMAC: Simultaneous Mapping and Clustering Using Spectral Decompositions
We introduce a principled approach for simultaneous mapping and clustering (SMAC) for establishing consistent maps across heterogeneous object collections (e. g., 2D images or 3D shapes).
Dynamic Filtering with Large Sampling Field for ConvNets
We propose a dynamic filtering strategy with large sampling field for ConvNets (LS-DFN), where the position-specific kernels learn from not only the identical position but also multiple sampled neighbor regions.
Translation Synchronization via Truncated Least Squares
In this paper, we introduce a robust algorithm, \textsl{TranSync}, for the 1D translation synchronization problem, in which the aim is to recover the global coordinates of a set of nodes from noisy measurements of relative coordinates along an observation graph.
Higher Order Mutual Information Approximation for Feature Selection
Feature selection is a process of choosing a subset of relevant features so that the quality of prediction models can be improved.
