1 citations · 1 across the 14 of their papers we have counts for
16 papers
Datasets for Valence and Arousal Inference: A Survey
Helen Schneider, Svetlana Pavlitska, Helen Gremmelmaier +1
Understanding human affect can be used in robotics, marketing, education, human-computer interaction, healthcare, entertainment, autonomous driving, and psychology to enhance decis…
Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers
Svetlana Pavlitska, Haixi Fan, Konstantin Ditschuneit +1
Robustifying convolutional neural networks (CNNs) against adversarial attacks remains challenging and often requires resource-intensive countermeasures. We explore the use of spars…
Extracting Uncertainty Estimates from Mixtures of Experts for Semantic Segmentation
Svetlana Pavlitska, Beyza Keskin, Alwin Faßbender +2
Estimating accurate and well-calibrated predictive uncertainty is important for enhancing the reliability of computer vision models, especially in safety-critical applications like…
EffiComm: Bandwidth Efficient Multi Agent Communication
Melih Yazgan, Allen Xavier Arasan, J. Marius Zöllner
Collaborative perception allows connected vehicles to exchange sensor information and overcome each vehicle's blind spots. Yet transmitting raw point clouds or full feature maps ov…
Why Braking? Scenario Extraction and Reasoning Utilizing LLM
Yin Wu, Daniel Slieter, Vivek Subramanian +3
The growing number of ADAS-equipped vehicles has led to a dramatic increase in driving data, yet most of them capture routine driving behavior. Identifying and understanding safety…
LanePerf: a Performance Estimation Framework for Lane Detection
Yin Wu, Daniel Slieter, Ahmed Abouelazm +2
Lane detection is a critical component of Advanced Driver-Assistance Systems (ADAS) and Automated Driving System (ADS), providing essential spatial information for lateral control.…