Search Results for author:
Found 16 papers, 6 papers with code
Leveraging Self-Supervised Instance Contrastive Learning for Radar Object Detection
In recent years, driven by the need for safer and more autonomous transport systems, the automotive industry has shifted toward integrating a growing number of Advanced Driver Assistance Systems (ADAS).
Variational Bayes image restoration with compressive autoencoders
Regularization of inverse problems is of paramount importance in computational imaging.
A recurrent CNN for online object detection on raw radar frames
Exploiting the time information (e. g., multiple frames) has been shown to help to capture better the dynamics of objects and, therefore, the variation in the shape of objects.
DAROD: A Deep Automotive Radar Object Detector on Range-Doppler maps
Due to the small number of raw data automotive radar datasets and the low resolution of such radar sensors, automotive radar object detection has been little explored with deep learning models in comparison to camera and lidar-based approaches.
Algorithms for audio inpainting based on probabilistic nonnegative matrix factorization
First, we treat the missing samples as latent variables, and derive two expectation-maximization algorithms for estimating the parameters of the model, depending on whether we formulate the problem in the time- or time-frequency domain.
Regularization via deep generative models: an analysis point of view
We illustrate the interest of such a formulation by running experiments of inpainting, deblurring and super-resolution.
Phase retrieval with Bregman divergences and application to audio signal recovery
Therefore, we formulate PR as a new minimization problem involving Bregman divergences.
Sound
Ordinal Non-negative Matrix Factorization for Recommendation
In particular, our algorithm preserves the scalability of PF and can be applied to huge sparse datasets.
Fast reconstruction of atomic-scale STEM-EELS images from sparse sampling
Besides, among the methods proposed in the microscopy literature, some are fast but inaccurate while others provide accurate reconstruction but at the price of a high computation burden.
Hyperspectral and multispectral image fusion under spectrally varying spatial blurs -- Application to high dimensional infrared astronomical imaging
Hyperspectral imaging has become a significant source of valuable data for astronomers over the past decades.
Recommendation from Raw Data with Adaptive Compound Poisson Factorization
Count data are often used in recommender systems: they are widespread (song play counts, product purchases, clicks on web pages) and can reveal user preference without any explicit rating from the user.
Factor analysis of dynamic PET images: beyond Gaussian noise
Factor analysis has proven to be a relevant tool for extracting tissue time-activity curves (TACs) in dynamic PET images, since it allows for an unsupervised analysis of the data.
Coupled dictionary learning for unsupervised change detection between multi-sensor remote sensing images
This paper addresses the problem of unsupervisedly detecting changes between two observed images acquired by sensors of different modalities with possibly different resolutions.
Reconstruction of partially sampled multi-band images - Application to STEM-EELS imaging
Electron microscopy has shown to be a very powerful tool to map the chemical nature of samples at various scales down to atomic resolution.
Negative Binomial Matrix Factorization for Recommender Systems
We introduce negative binomial matrix factorization (NBMF), a matrix factorization technique specially designed for analyzing over-dispersed count data.
Unmixing dynamic PET images with variable specific binding kinetics
Modeling the variability of the specific binding factor has a strong potential impact for dynamic PET image analysis.
