13 citations · 43 across the 17 of their papers we have counts for
10 papers · 1 filter
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…
Benchmarking Vision Foundation Models for Input Monitoring in Autonomous Driving
Mert Keser, Halil Ibrahim Orhan, Niki Amini-Naieni +3
Deep neural networks (DNNs) remain challenged by distribution shifts in complex open-world domains like automated driving (AD): Robustness against yet unknown novel objects (semant…
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…
Concept-Based Explanations in Computer Vision: Where Are We and Where Could We Go?
Jae Hee Lee, Georgii Mikriukov, Gesina Schwalbe +2
Concept-based XAI (C-XAI) approaches to explaining neural vision models are a promising field of research, since explanations that refer to concepts (i.e., semantically meaningful…
Investigating Calibration and Corruption Robustness of Post-hoc Pruned Perception CNNs: An Image Classification Benchmark Study
Pallavi Mitra, Gesina Schwalbe, Nadja Klein
Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in many computer vision tasks. However, high computational and storage demands hinder their deployme…
Local Concept Embeddings for Analysis of Concept Distributions in Vision DNN Feature Spaces
Georgii Mikriukov, Gesina Schwalbe, Korinna Bade
Insights into the learned latent representations are imperative for verifying deep neural networks (DNNs) in critical computer vision (CV) tasks. Therefore, state-of-the-art superv…