61 citations · 266 across the 36 of their papers we have counts for
46 papers
Generalization by Adaptation: Diffusion-Based Domain Extension for Domain-Generalized Semantic Segmentation
Joshua Niemeijer, Manuel Schwonberg, Jan-Aike Termöhlen +2
When models, e.g., for semantic segmentation, are applied to images that are vastly different from training data, the performance will drop significantly. Domain adaptation methods…
Employing Real Training Data for Deep Noise Suppression
Ziyi Xu, Marvin Sach, Jan Pirklbauer +1
Most deep noise suppression (DNS) models are trained with reference-based losses requiring access to clean speech. However, sometimes an additive microphone model is insufficient f…
A Re-Parameterized Vision Transformer (ReVT) for Domain-Generalized Semantic Segmentation
Jan-Aike Termöhlen, Timo Bartels, Tim Fingscheidt
The task of semantic segmentation requires a model to assign semantic labels to each pixel of an image. However, the performance of such models degrades when deployed in an unseen…
Efficient Acoustic Echo Suppression with Condition-Aware Training
Ernst Seidel, Pejman Mowlaee, Tim Fingscheidt
The topic of deep acoustic echo control (DAEC) has seen many approaches with various model topologies in recent years. Convolutional recurrent networks (CRNs), consisting of a conv…
EffCRN: An Efficient Convolutional Recurrent Network for High-Performance Speech Enhancement
Marvin Sach, Jan Franzen, Bruno Defraene +4
Fully convolutional recurrent neural networks (FCRNs) have shown state-of-the-art performance in single-channel speech enhancement. However, the number of parameters and the FLOPs/…
Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving
Manuel Schwonberg, Joshua Niemeijer, Jan-Aike Termöhlen +4
Deep neural networks (DNNs) have proven their capabilities in many areas in the past years, such as robotics, or automated driving, enabling technological breakthroughs. DNNs play…