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Found 27 papers, 12 papers with code
Modeling uncertainty for Gaussian Splatting
We present Stochastic Gaussian Splatting (SGS): the first framework for uncertainty estimation using Gaussian Splatting (GS).
Onboard deep lossless and near-lossless predictive coding of hyperspectral images with line-based attention
Deep learning methods have traditionally been difficult to apply to compression of hyperspectral images onboard of spacecrafts, due to the large computational complexity needed to achieve adequate representational power, as well as the lack of suitable datasets for training and testing.
Deep 3D World Models for Multi-Image Super-Resolution Beyond Optical Flow
Multi-image super-resolution (MISR) allows to increase the spatial resolution of a low-resolution (LR) acquisition by combining multiple images carrying complementary information in the form of sub-pixel offsets in the scene sampling, and can be significantly more effective than its single-image counterpart.
Fast Inference in Denoising Diffusion Models via MMD Finetuning
Our findings show that the proposed method is able to produce high-quality samples in a fraction of the time required by widely-used diffusion models, and outperforms state-of-the-art techniques for accelerated sampling.
Rethinking the compositionality of point clouds through regularization in the hyperbolic space
In this paper, we propose to embed the features of a point cloud classifier into the hyperbolic space and explicitly regularize the space to account for the part-whole hierarchy.
Ranked #9 on
3D Point Cloud Classification
on ModelNet40
Cross-modal Learning for Image-Guided Point Cloud Shape Completion
In this paper we explore the recent topic of point cloud completion, guided by an auxiliary image.
Ranked #1 on
Point Cloud Completion
on ShapeNet-ViPC
Exploring the solution space of linear inverse problems with GAN latent geometry
Inverse problems consist in reconstructing signals from incomplete sets of measurements and their performance is highly dependent on the quality of the prior knowledge encoded via regularization.
Super-resolved multi-temporal segmentation with deep permutation-invariant networks
Multi-image super-resolution from multi-temporal satellite acquisitions of a scene has recently enjoyed great success thanks to new deep learning models.
Semi-supervised learning for joint SAR and multispectral land cover classification
Semi-supervised learning techniques are gaining popularity due to their capability of building models that are effective, even when scarce amounts of labeled data are available.
Permutation invariance and uncertainty in multitemporal image super-resolution
However, existing models have neglected the issue of temporal permutation, whereby the temporal ordering of the input images does not carry any relevant information for the super-resolution task and causes such models to be inefficient with the, often scarce, ground truth data that available for training.
Ranked #2 on
Multi-Frame Super-Resolution
on PROBA-V
Denoise and Contrast for Category Agnostic Shape Completion
The combined embedding inherits category-agnostic properties from the chosen pretext tasks.
RAN-GNNs: breaking the capacity limits of graph neural networks
In this paper, we investigate the recently proposed randomly wired architectures in the context of graph neural networks.
Ranked #20 on
Graph Property Prediction
on ogbg-molpcba
Don't stack layers in graph neural networks, wire them randomly
In this paper, we investigate the recently proposed randomly wired architectures in the context of graph neural networks.
Deep Learning Methods For Synthetic Aperture Radar Image Despeckling: An Overview Of Trends And Perspectives
Synthetic aperture radar (SAR) images are affected by a spatially-correlated and signal-dependent noise called speckle, which is very severe and may hinder image exploitation.
Learning Graph-Convolutional Representations for Point Cloud Denoising
Point clouds are an increasingly relevant data type but they are often corrupted by noise.
Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networks
Information extraction from synthetic aperture radar (SAR) images is heavily impaired by speckle noise, hence despeckling is a crucial preliminary step in scene analysis algorithms.
DeepSUM++: Non-local Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images
Deep learning methods for super-resolution of a remote sensing scene from multiple unregistered low-resolution images have recently gained attention thanks to a challenge proposed by the European Space Agency.
Towards Deep Unsupervised SAR Despeckling with Blind-Spot Convolutional Neural Networks
The proposed method is trained employing only noisy images and can therefore learn features of real SAR images rather than synthetic data.
Analysis of SparseHash: an efficient embedding of set-similarity via sparse projections
Embeddings provide compact representations of signals in order to perform efficient inference in a wide variety of tasks.
Deep Graph-Convolutional Image Denoising
The graph convolution operation generalizes the classic convolution to arbitrary graphs.
Ranked #3 on
Grayscale Image Denoising
on Set12 sigma25
DeepSUM: Deep neural network for Super-resolution of Unregistered Multitemporal images
This novel framework integrates the spatial registration task directly inside the CNN, and allows to exploit the representation learning capabilities of the network to enhance registration accuracy.
Ranked #8 on
Multi-Frame Super-Resolution
on PROBA-V
High-throughput Onboard Hyperspectral Image Compression with Ground-based CNN Reconstruction
Compression of hyperspectral images onboard of spacecrafts is a tradeoff between the limited computational resources and the ever-growing spatial and spectral resolution of the optical instruments.
Image Denoising with Graph-Convolutional Neural Networks
The graph-convolutional layers dynamically construct neighborhoods in the feature space to detect latent correlations in the feature maps produced by the hidden layers.
Learning Localized Generative Models for 3D Point Clouds via Graph Convolution
We also study the problem of defining an upsampling layer in the graph-convolutional generator, such that it learns to exploit a self-similarity prior on the data distribution to sample more effectively.
Binary adaptive embeddings from order statistics of random projections
We use some of the largest order statistics of the random projections of a reference signal to construct a binary embedding that is adapted to signals correlated with such signal.
Smoothness-Constrained Image Recovery from Block-Based Random Projections
On one hand, it allows to enforce a set of constraints to drive the reconstruction algorithm towards a smooth solution, imposing the similarity of block borders.
Spatially Scalable Compressed Image Sensing with Hybrid Transform and Inter-layer Prediction Model
The proposed method successfully provides resolution and quality scalability with modest complexity and it provides gains in the quality of the reconstructed images with respect to separate encoding of the quality layers.
