1 citations · 1 across the 13 of their papers we have counts for
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Doppio: A Dataset for Contactless Weight Estimation of Falling Particles
Simon Kiefhaber, Jan-Martin O. Steitz, Julia Grabinski +5
Measuring the mass of powder, including falling particles, is a common task in industrial applications. While scales are effective for static measurements, many applications requir…
WiFlow: Estimating Optical Flow using WiFi Channel State Information
Thomas Weigel, Simon Kiefhaber, Fabian Portner +2
Knowing where and how fast objects are moving within a scene is important across various domains. Usually, cameras are used to capture the data necessary for this task, but adding…
Interpretability Without Tradeoffs: Disentangling Polysemanticity At Equal Predictive Performance
Doğukan Bağcı, Bernt Schiele, Simone Schaub-Meyer +2
Deep neural networks (DNNs) are widely used, but interpreting what they actually learn remains difficult. A major obstacle is that individual neurons often encode multiple unrelate…
What is Missing? Explaining Neurons Activated by Absent Concepts
Robin Hesse, Simone Schaub-Meyer, Janina Hesse +2
Explainable artificial intelligence (XAI) aims to provide human-interpretable insights into the behavior of deep neural networks (DNNs), typically by estimating a simplified causal…
Evaluating Object-Centric Models beyond Object Discovery
Krishnakant Singh, Simone Schaub-Meyer, Stefan Roth
Object-centric learning (OCL) aims to learn structured scene representations that support compositional generalization and robustness to out-of-distribution (OOD) data. However, OC…
MUFASA: A Multi-Layer Framework for Slot Attention
Sebastian Bock, Leonie Schüßler, Krishnakant Singh +2
Unsupervised object-centric learning (OCL) decomposes visual scenes into distinct entities. Slot attention is a popular approach that represents individual objects as latent vector…