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Found 6 papers, 1 papers with code
Contrastive Learning-Based Spectral Knowledge Distillation for Multi-Modality and Missing Modality Scenarios in Semantic Segmentation
To address this, a novel multi-modal fusion approach called CSK-Net is proposed, which uses a contrastive learning-based spectral knowledge distillation technique along with an automatic mixed feature exchange mechanism for semantic segmentation in optical (EO) and infrared (IR) images.
MRFP: Learning Generalizable Semantic Segmentation from Sim-2-Real with Multi-Resolution Feature Perturbation
Deep neural networks have shown exemplary performance on semantic scene understanding tasks on source domains, but due to the absence of style diversity during training, enhancing performance on unseen target domains using only single source domain data remains a challenging task.
DeepMAO: Deep Multi-scale Aware Overcomplete Network for Building Segmentation in Satellite Imagery
Experimental results on SpaceNet 6 dataset, on both EO and SAR modalities, and the INRIA dataset show that DeepMAO achieves state-of-the-art building segmentation performance, including small and complex-shaped buildings with a negligible increase in the parameter count.
Segmentation
The Semantic Segmentation Of Remote Sensing Imagery
Fully complex-valued deep learning model for visual perception
This paper proposes that operating entirely in the complex domain increases the overall performance of complex-valued models.
Fully Complex-valued Fully Convolutional Multi-feature Fusion Network (FC2MFN) for Building Segmentation of InSAR images
Building segmentation in high-resolution InSAR images is a challenging task that can be useful for large-scale surveillance.
Multi-Modal Domain Fusion for Multi-modal Aerial View Object Classification
Using just aerial view Electro-optical(EO) images for ATR systems may also not result in high accuracy as these images are of low resolution and also do not provide ample information in extreme weather conditions.
