209 citations · 239 across the 10 of their papers we have counts for
14 papers · 1 filter
Label Noise Robust Image Representation Learning based on Supervised Variational Autoencoders in Remote Sensing
Gencer Sumbul, Begüm Demir
Due to the publicly available thematic maps and crowd-sourced data, remote sensing (RS) image annotations can be gathered at zero cost for training deep neural networks (DNNs). How…
Multi-Modal Fusion Transformer for Visual Question Answering in Remote Sensing
Tim Siebert, Kai Norman Clasen, Mahdyar Ravanbakhsh +1
With the new generation of satellite technologies, the archives of remote sensing (RS) images are growing very fast. To make the intrinsic information of each RS image easily acces…
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…
Weakly Supervised Semantic Segmentation of Remote Sensing Images for Tree Species Classification Based on Explanation Methods
Steve Ahlswede, Nimisha Thekke-Madam, Christian Schulz +2
The collection of a high number of pixel-based labeled training samples for tree species identification is time consuming and costly in operational forestry applications. To addres…