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Found 4 papers, 4 papers with code
DISIR: Deep Image Segmentation with Interactive Refinement
Starting from an initial output based on the image only, our network then interactively refines this segmentation map using a concatenation of the image and user annotations.
Interactive Learning for Semantic Segmentation in Earth Observation
Dense pixel-wise classification maps output by deep neural networks are of extreme importance for scene understanding.
DIAL: Deep Interactive and Active Learning for Semantic Segmentation in Remote Sensing
We propose in this article to build up a collaboration between a deep neural network and a human in the loop to swiftly obtain accurate segmentation maps of remote sensing images.
Weakly-supervised continual learning for class-incremental segmentation
Transfer learning is a powerful way to adapt existing deep learning models to new emerging use-cases in remote sensing.
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