48 citations · 109 across the 9 of their papers we have counts for
13 papers
Controllable Inversion of Black-Box Face Recognition Models via Diffusion
Manuel Kansy, Anton Raël, Graziana Mignone +4
Face recognition models embed a face image into a low-dimensional identity vector containing abstract encodings of identity-specific facial features that allow individuals to be di…
Microdosing: Knowledge Distillation for GAN based Compression
Leonhard Helminger, Roberto Azevedo, Abdelaziz Djelouah +2
Recently, significant progress has been made in learned image and video compression. In particular the usage of Generative Adversarial Networks has lead to impressive results in th…
Blind Image Restoration with Flow Based Priors
Leonhard Helminger, Michael Bernasconi, Abdelaziz Djelouah +2
Image restoration has seen great progress in the last years thanks to the advances in deep neural networks. Most of these existing techniques are trained using full supervision wit…
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…
Enriching Video Captions With Contextual Text
Philipp Rimle, Pelin Dogan, Markus Gross
Understanding video content and generating caption with context is an important and challenging task. Unlike prior methods that typically attempt to generate generic video captions…
Shapley Value as Principled Metric for Structured Network Pruning
Marco Ancona, Cengiz Öztireli, Markus Gross
Structured pruning is a well-known technique to reduce the storage size and inference cost of neural networks. The usual pruning pipeline consists of ranking the network internal f…
Neural Sequential Phrase Grounding (SeqGROUND)
Pelin Dogan, Leonid Sigal, Markus Gross
We propose an end-to-end approach for phrase grounding in images. Unlike prior methods that typically attempt to ground each phrase independently by building an image-text embeddin…
Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximation
Marco Ancona, Cengiz Öztireli, Markus Gross
The problem of explaining the behavior of deep neural networks has recently gained a lot of attention. While several attribution methods have been proposed, most come without stron…
Disentangled Dynamic Representations from Unordered Data
Leonhard Helminger, Abdelaziz Djelouah, Markus Gross +1
We present a deep generative model that learns disentangled static and dynamic representations of data from unordered input. Our approach exploits regularities in sequential data t…
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…
Ten Years of Research on Intelligent Educational Games for Learning Spelling and Mathematics
Barbara Solenthaler, Severin Klingler, Tanja Käser +1
In this article, we present our findings from ten years of research on intelligent educational games. We discuss the architecture of our training environments for learning spelling…
A Neural Multi-sequence Alignment TeCHnique (NeuMATCH)
Pelin Dogan, Boyang Li, Leonid Sigal +1
The alignment of heterogeneous sequential data (video to text) is an important and challenging problem. Standard techniques for this task, including Dynamic Time Warping (DTW) and…
Towards better understanding of gradient-based attribution methods for Deep Neural Networks
Marco Ancona, Enea Ceolini, Cengiz Öztireli +1
Understanding the flow of information in Deep Neural Networks (DNNs) is a challenging problem that has gain increasing attention over the last few years. While several methods have…