21 citations · 23 across the 6 of their papers we have counts for
6 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…
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…
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…