Search Results for author:
Found 8 papers, 3 papers with code
SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation
In recent years, semantic segmentation has become a pivotal tool in processing and interpreting satellite imagery.
G-MSM: Unsupervised Multi-Shape Matching with Graph-based Affinity Priors
We present G-MSM (Graph-based Multi-Shape Matching), a novel unsupervised learning approach for non-rigid shape correspondence.
A Unified Framework for Implicit Sinkhorn Differentiation
The Sinkhorn operator has recently experienced a surge of popularity in computer vision and related fields.
DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation
These observations are paired with pixel-wise monthly semantic segmentation labels of 7 land use and land cover (LULC) classes.
Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images
It uses this model to analyze differences in the pixel and its spatial context-based predictions in subsequent time periods for change detection.
Scalable Sinkhorn Backpropagation
Our main contribution is deriving a simple and efficient algorithm that performs this backward pass in closed form.
Coming Down to Earth: Satellite-to-Street View Synthesis for Geo-Localization
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.
Deep Shells: Unsupervised Shape Correspondence with Optimal Transport
We propose a novel unsupervised learning approach to 3D shape correspondence that builds a multiscale matching pipeline into a deep neural network.
