336 citations · 1.4k across the 70 of their papers we have counts for
6 papers · 1 filter
Mixture of Inference Networks for VAE-based Audio-visual Speech Enhancement
Mostafa Sadeghi, Xavier Alameda-Pineda
In this paper, we are interested in unsupervised (unknown noise) audio-visual speech enhancement based on variational autoencoders (VAEs), where the probability distribution of cle…
Robust Unsupervised Audio-visual Speech Enhancement Using a Mixture of Variational Autoencoders
Mostafa Sadeghi, Xavier Alameda-Pineda
Recently, an audio-visual speech generative model based on variational autoencoder (VAE) has been proposed, which is combined with a nonnegative matrix factorization (NMF) model fo…
A Recurrent Variational Autoencoder for Speech Enhancement
Simon Leglaive, Xavier Alameda-Pineda, Laurent Girin +1
This paper presents a generative approach to speech enhancement based on a recurrent variational autoencoder (RVAE). The deep generative speech model is trained using clean speech…
Audio-visual Speech Enhancement Using Conditional Variational Auto-Encoders
Mostafa Sadeghi, Simon Leglaive, Xavier Alameda-PIneda +2
Variational auto-encoders (VAEs) are deep generative latent variable models that can be used for learning the distribution of complex data. VAEs have been successfully used to lear…
How To Train Your Deep Multi-Object Tracker
Yihong Xu, Aljosa Osep, Yutong Ban +3
The recent trend in vision-based multi-object tracking (MOT) is heading towards leveraging the representational power of deep learning to jointly learn to detect and track objects.…
CANU-ReID: A Conditional Adversarial Network for Unsupervised person Re-IDentification
Guillaume Delorme, Yihong Xu, Stephane Lathuilière +2
Unsupervised person re-ID is the task of identifying people on a target data set for which the ID labels are unavailable during training. In this paper, we propose to unify two tre…