53 citations · 120 across the 8 of their papers we have counts for
7 papers · 1 filter
Leveraging Diffusion Models for Stylization using Multiple Style Images
Dan Ruta, Abdelaziz Djelouah, Raphael Ortiz +1
Recent advances in latent diffusion models have enabled exciting progress in image style transfer. However, several key issues remain. For example, existing methods still struggle…
Lossy Image Compression with Normalizing Flows
Leonhard Helminger, Abdelaziz Djelouah, Markus Gross +1
Deep learning based image compression has recently witnessed exciting progress and in some cases even managed to surpass transform coding based approaches that have been establishe…
Content Adaptive Optimization for Neural Image Compression
Joaquim Campos, Simon Meierhans, Abdelaziz Djelouah +1
The field of neural image compression has witnessed exciting progress as recently proposed architectures already surpass the established transform coding based approaches. While, s…
Deep Video Color Propagation
Simone Meyer, Victor Cornillère, Abdelaziz Djelouah +2
Traditional approaches for color propagation in videos rely on some form of matching between consecutive video frames. Using appearance descriptors, colors are then propagated both…
On Regularized Losses for Weakly-supervised CNN Segmentation
Meng Tang, Federico Perazzi, Abdelaziz Djelouah +3
Minimization of regularized losses is a principled approach to weak supervision well-established in deep learning, in general. However, it is largely overlooked in semantic segment…
Normalized Cut Loss for Weakly-supervised CNN Segmentation
Meng Tang, Abdelaziz Djelouah, Federico Perazzi +2
Most recent semantic segmentation methods train deep convolutional neural networks with fully annotated masks requiring pixel-accuracy for good quality training. Common weakly-supe…
PhaseNet for Video Frame Interpolation
Simone Meyer, Abdelaziz Djelouah, Brian McWilliams +3
Most approaches for video frame interpolation require accurate dense correspondences to synthesize an in-between frame. Therefore, they do not perform well in challenging scenarios…