21 citations · 39 across the 12 of their papers we have counts for
12 papers
Uncertainty Gating for Cost-Aware Explainable Artificial Intelligence
Georgii Mikriukov, Grégoire Montavon, Marina M. -C. Höhne
Post-hoc explanation methods are widely used to interpret black-box predictions, but their generation is often computationally expensive and their reliability is not guaranteed. We…
Explaining, Verifying, and Aligning Semantic Hierarchies in Vision-Language Model Embeddings
Gesina Schwalbe, Mert Keser, Moritz Bayerkuhnlein +8
Vision-language model (VLM) encoders such as CLIP enable strong retrieval and zero-shot classification in a shared image-text embedding space, yet the semantic organization of this…
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…
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…
Locally Testing Model Detections for Semantic Global Concepts
Franz Motzkus, Georgii Mikriukov, Christian Hellert +1
Ensuring the quality of black-box Deep Neural Networks (DNNs) has become ever more significant, especially in safety-critical domains such as automated driving. While global concep…
The Anatomy of Adversarial Attacks: Concept-based XAI Dissection
Georgii Mikriukov, Gesina Schwalbe, Franz Motzkus +1
Adversarial attacks (AAs) pose a significant threat to the reliability and robustness of deep neural networks. While the impact of these attacks on model predictions has been exten…
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…
Revealing Similar Semantics Inside CNNs: An Interpretable Concept-based Comparison of Feature Spaces
Georgii Mikriukov, Gesina Schwalbe, Christian Hellert +1
Safety-critical applications require transparency in artificial intelligence (AI) components, but widely used convolutional neural networks (CNNs) widely used for perception tasks…
Evaluating the Stability of Semantic Concept Representations in CNNs for Robust Explainability
Georgii Mikriukov, Gesina Schwalbe, Christian Hellert +1
Analysis of how semantic concepts are represented within Convolutional Neural Networks (CNNs) is a widely used approach in Explainable Artificial Intelligence (XAI) for interpretin…
Unsupervised Contrastive Hashing for Cross-Modal Retrieval in Remote Sensing
Georgii Mikriukov, Mahdyar Ravanbakhsh, Begüm Demir
The development of cross-modal retrieval systems that can search and retrieve semantically relevant data across different modalities based on a query in any modality has attracted…
An Unsupervised Cross-Modal Hashing Method Robust to Noisy Training Image-Text Correspondences in Remote Sensing
Georgii Mikriukov, Mahdyar Ravanbakhsh, Begüm Demir
The development of accurate and scalable cross-modal image-text retrieval methods, where queries from one modality (e.g., text) can be matched to archive entries from another (e.g.…
Deep Unsupervised Contrastive Hashing for Large-Scale Cross-Modal Text-Image Retrieval in Remote Sensing
Georgii Mikriukov, Mahdyar Ravanbakhsh, Begüm Demir
Due to the availability of large-scale multi-modal data (e.g., satellite images acquired by different sensors, text sentences, etc) archives, the development of cross-modal retriev…