4 papers · 1 filter
Improving 3D Foot Motion Reconstruction in Markerless Monocular Human Motion Capture
Tom Wehrbein, Bodo Rosenhahn
State-of-the-art methods can recover accurate overall 3D human body motion from in-the-wild videos. However, they often fail to capture fine-grained articulations, especially in th…
Interpretable Decision-Making for End-to-End Autonomous Driving
Mona Mirzaie, Bodo Rosenhahn
Trustworthy AI is mandatory for the broad deployment of autonomous vehicles. Although end-to-end approaches derive control commands directly from raw data, interpreting these decis…
UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model
Timo Kaiser, Thomas Norrenbrock, Bodo Rosenhahn
The introduction of the Segment Anything Model (SAM) has paved the way for numerous semantic segmentation applications. For several tasks, quantifying the uncertainty of SAM is of…
QPM: Discrete Optimization for Globally Interpretable Image Classification
Thomas Norrenbrock, Timo Kaiser, Sovan Biswas +2
Understanding the classifications of deep neural networks, e.g. used in safety-critical situations, is becoming increasingly important. While recent models can locally explain a si…