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Found 7 papers, 1 papers with code
Gradient-based Trajectory Optimization with Parallelized Differentiable Traffic Simulation
We show that we can use the simulator to filter noise in the input trajectories (trajectory filtering), reconstruct dense trajectories from sparse ones (trajectory reconstruction), and predict future trajectories (trajectory prediction), with all generated trajectories adhering to physical laws.
GeoWATCH for Detecting Heavy Construction in Heterogeneous Time Series of Satellite Images
Learning from multiple sensors is challenging due to spatio-temporal misalignment and differences in resolution and captured spectra.
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.
