5 citations · 5 across the 6 of their papers we have counts for
5 papers · 1 filter
LFA: Layer Feature Attention for Run-Time Introspection of 2D Object Detectors in Automated Driving
Mert Keser, Alois Knoll
Reliable object detection is critical for automated driving, yet even state-of-the-art detectors inevitably make errors that can compromise safety. Introspection methods that predi…
On Background Bias of Post-Hoc Concept Embeddings in Computer Vision DNNs
Gesina Schwalbe, Georgii Mikriukov, Edgar Heinert +5
The thriving research field of concept-based explainable artificial intelligence (C-XAI) investigates how human-interpretable semantic concepts embed in the latent spaces of deep n…
Unveiling Ontological Commitment in Multi-Modal Foundation Models
Mert Keser, Gesina Schwalbe, Niki Amini-Naieni +2
Ontological commitment, i.e., used concepts, relations, and assumptions, are a corner stone of qualitative reasoning (QR) models. The state-of-the-art for processing raw inputs, th…
How Could Generative AI Support Compliance with the EU AI Act? A Review for Safe Automated Driving Perception
Mert Keser, Youssef Shoeb, Alois Knoll
Deep Neural Networks (DNNs) have become central for the perception functions of autonomous vehicles, substantially enhancing their ability to understand and interpret the environme…
Content Disentanglement for Semantically Consistent Synthetic-to-Real Domain Adaptation
Mert Keser, Artem Savkin, Federico Tombari
Synthetic data generation is an appealing approach to generate novel traffic scenarios in autonomous driving. However, deep learning perception algorithms trained solely on synthet…