20 citations · 22 across the 5 of their papers we have counts for
7 papers
Gradient-based Trajectory Optimization with Parallelized Differentiable Traffic Simulation
Sanghyun Son, Laura Zheng, Brian Clipp +3
We present a parallelized differentiable traffic simulator based on the Intelligent Driver Model (IDM), a car-following framework that incorporates driver behavior as key variables…
GeoWATCH for Detecting Heavy Construction in Heterogeneous Time Series of Satellite Images
Jon Crall, Connor Greenwell, David Joy +3
Learning from multiple sensors is challenging due to spatio-temporal misalignment and differences in resolution and captured spectra. To that end, we introduce GeoWATCH, a flexible…
Contrastive Cross-Modal Pre-Training: A General Strategy for Small Sample Medical Imaging
Gongbo Liang, Connor Greenwell, Yu Zhang +3
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. In contrast, tex…
Single Image Cloud Detection via Multi-Image Fusion
Scott Workman, M. Usman Rafique, Hunter Blanton +2
Artifacts in imagery captured by remote sensing, such as clouds, snow, and shadows, present challenges for various tasks, including semantic segmentation and object detection. A pr…
Learning Geo-Temporal Image Features
Menghua Zhai, Tawfiq Salem, Connor Greenwell +3
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…
Learning to Map Nearly Anything
Tawfiq Salem, Connor Greenwell, Hunter Blanton +1
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. Historically, such task…
What Goes Where: Predicting Object Distributions from Above
Connor Greenwell, Scott Workman, Nathan Jacobs
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 image…