1 citations · 1 across the 7 of their papers we have counts for
4 papers · 1 filter
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…
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.…
Contrast & Compress: Learning Lightweight Embeddings for Short Trajectories
Abhishek Vivekanandan, Christian Hubschneider, J. Marius Zöllner
The ability to retrieve semantically and directionally similar short-range trajectories with both accuracy and efficiency is foundational for downstream applications such as motion…
Sparsely-gated Mixture-of-Expert Layers for CNN Interpretability
Svetlana Pavlitska, Christian Hubschneider, Lukas Struppek +1
Sparsely-gated Mixture of Expert (MoE) layers have been recently successfully applied for scaling large transformers, especially for language modeling tasks. An intriguing side eff…