12 papers
MUFASA: A Multi-Layer Framework for Slot Attention
Sebastian Bock, Leonie SchüÃler, 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…
Interpretability Without Tradeoffs: Disentangling Polysemanticity At Equal Predictive Performance
DoÄukan BaÄcı, 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…
Beyond Accuracy: What Matters in Designing Well-Behaved Image Classification Models?
Robin Hesse, DoÄukan BaÄcı, Bernt Schiele +2
Deep learning has become an essential part of computer vision, with deep neural networks (DNNs) excelling in predictive performance. However, they often fall short in other critica…
Multimodal Knowledge Distillation for Egocentric Action Recognition Robust to Missing Modalities
Maria Santos-Villafranca, Dustin Carrión-Ojeda, Alejandro Perez-Yus +3
Egocentric action recognition enables robots to facilitate human-robot interactions and monitor task progress. Existing methods often rely solely on RGB videos, although additional…
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…