33 citations · 77 across the 10 of their papers we have counts for
8 papers · 1 filter
SATGround: A Spatially-Aware Approach for Visual Grounding in Remote Sensing
Aysim Toker, Andreea-Maria Oncescu, Roy Miles +2
Vision-language models (VLMs) are emerging as powerful generalist tools for remote sensing, capable of integrating information across diverse tasks and enabling flexible, instructi…
SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation
Aysim Toker, Marvin Eisenberger, Daniel Cremers +1
In recent years, semantic segmentation has become a pivotal tool in processing and interpreting satellite imagery. Yet, a prevalent limitation of supervised learning techniques rem…
G-MSM: Unsupervised Multi-Shape Matching with Graph-based Affinity Priors
Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé +1
We present G-MSM (Graph-based Multi-Shape Matching), a novel unsupervised learning approach for non-rigid shape correspondence. Rather than treating a collection of input poses as…
A Unified Framework for Implicit Sinkhorn Differentiation
Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé +2
The Sinkhorn operator has recently experienced a surge of popularity in computer vision and related fields. One major reason is its ease of integration into deep learning framework…
DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation
Aysim Toker, Lukas Kondmann, Mark Weber +10
Earth observation is a fundamental tool for monitoring the evolution of land use in specific areas of interest. Observing and precisely defining change, in this context, requires b…
Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images
Lukas Kondmann, Aysim Toker, Sudipan Saha +3
Detecting changes on the ground in multitemporal Earth observation data is one of the key problems in remote sensing. In this paper, we introduce Sibling Regression for Optical Cha…
Coming Down to Earth: Satellite-to-Street View Synthesis for Geo-Localization
Aysim Toker, Qunjie Zhou, Maxim Maximov +1
The goal of cross-view image based geo-localization is to determine the location of a given street view image by matching it against a collection of geo-tagged satellite images. Th…
Deep Shells: Unsupervised Shape Correspondence with Optimal Transport
Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé +1
We propose a novel unsupervised learning approach to 3D shape correspondence that builds a multiscale matching pipeline into a deep neural network. This approach is based on smooth…