61 citations · 277 across the 41 of their papers we have counts for
9 papers · 1 filter
Relaxed Attention for Transformer Models
Timo Lohrenz, Björn Möller, Zhengyang Li +1
The powerful modeling capabilities of all-attention-based transformer architectures often cause overfitting and - for natural language processing tasks - lead to an implicitly lear…
On the Choice of Data for Efficient Training and Validation of End-to-End Driving Models
Marvin Klingner, Konstantin Müller, Mona Mirzaie +3
The emergence of data-driven machine learning (ML) has facilitated significant progress in many complicated tasks such as highly-automated driving. While much effort is put into im…
Amodal Cityscapes: A New Dataset, its Generation, and an Amodal Semantic Segmentation Challenge Baseline
Jasmin Breitenstein, Tim Fingscheidt
Amodal perception terms the ability of humans to imagine the entire shapes of occluded objects. This gives humans an advantage to keep track of everything that is going on, especia…
Does a PESQNet (Loss) Require a Clean Reference Input? The Original PESQ Does, But ACR Listening Tests Don't
Ziyi Xu, Maximilian Strake, Tim Fingscheidt
Perceptual evaluation of speech quality (PESQ) requires a clean speech reference as input, but predicts the results from (reference-free) absolute category rating (ACR) tests. In t…
Bandwidth-Scalable Fully Mask-Based Deep FCRN Acoustic Echo Cancellation and Postfiltering
Ernst Seidel, Rasmus Kongsgaard Olsson, Karim Haddad +3
Although today's speech communication systems support various bandwidths from narrowband to super-wideband and beyond, state-of-the art DNN methods for acoustic echo cancellation (…
Detecting Adversarial Perturbations in Multi-Task Perception
Marvin Klingner, Varun Ravi Kumar, Senthil Yogamani +2
While deep neural networks (DNNs) achieve impressive performance on environment perception tasks, their sensitivity to adversarial perturbations limits their use in practical appli…
Continual BatchNorm Adaptation (CBNA) for Semantic Segmentation
Marvin Klingner, Mouadh Ayache, Tim Fingscheidt
Environment perception in autonomous driving vehicles often heavily relies on deep neural networks (DNNs), which are subject to domain shifts, leading to a significantly decreased…
Improving Performance of Semantic Segmentation CycleGANs by Noise Injection into the Latent Segmentation Space
Jonas Löhdefink, Tim Fingscheidt
In recent years, semantic segmentation has taken benefit from various works in computer vision. Inspired by the very versatile CycleGAN architecture, we combine semantic segmentati…
Reconfigurable Intelligent Surface Enabled Spatial Multiplexing with Fully Convolutional Network
Bile Peng, Jan-Aike Termöhlen, Cong Sun +4
Reconfigurable intelligent surface (RIS) is an emerging technology for future wireless communication systems. In this work, we consider downlink spatial multiplexing enabled by the…