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Found 5 papers, 0 papers with code
Contrastive Cross-Modal Pre-Training: A General Strategy for Small Sample Medical Imaging
A key challenge in training neural networks for a given medical imaging task is often the difficulty of obtaining a sufficient number of manually labeled examples.
Single Image Cloud Detection via Multi-Image Fusion
A primary challenge in developing algorithms for identifying such artifacts is the cost of collecting annotated training data.
Learning Geo-Temporal Image Features
We propose to implicitly learn to extract geo-temporal image features, which are mid-level features related to when and where an image was captured, by explicitly optimizing for a set of location and time estimation tasks.
Learning to Map Nearly Anything
Looking at the world from above, it is possible to estimate many properties of a given location, including the type of land cover and the expected land use.
What Goes Where: Predicting Object Distributions from Above
In this work, we propose a cross-view learning approach, in which images captured from a ground-level view are used as weakly supervised annotations for interpreting overhead imagery.
