activity
20172021
most citedGenerating Long-term Trajectories Using Deep Hierarchical Networks

72 citations · 80 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2021

Residue Density Segmentation for Monitoring and Optimizing Tillage Practices

Jennifer Hobbs, Ivan Dozier, Naira Hovakimyan

"No-till" and cover cropping are often identified as the leading simple, best management practices for carbon sequestration in agriculture. However, the root of the problem is more…

cs.CV20206 cited

The 1st Agriculture-Vision Challenge: Methods and Results

Mang Tik Chiu, Xingqian Xu, Kai Wang +39

The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially f…

cs.LG20192 cited

Improved Structural Discovery and Representation Learning of Multi-Agent Data

Jennifer Hobbs, Matthew Holbrook, Nathan Frank +2

Central to all machine learning algorithms is data representation. For multi-agent systems, selecting a representation which adequately captures the interactions among agents is ch…

cs.LG2019

Rugby-Bot: Utilizing Multi-Task Learning & Fine-Grained Features for Rugby League Analysis

Matthew Holbrook, Jennifer Hobbs, Patrick Lucey

Sporting events are extremely complex and require a multitude of metrics to accurate describe the event. When making multiple predictions, one should make them from a single source…

cs.LG201772 cited

Generating Long-term Trajectories Using Deep Hierarchical Networks

Stephan Zheng, Yisong Yue, Patrick Lucey

We study the problem of modeling spatiotemporal trajectories over long time horizons using expert demonstrations. For instance, in sports, agents often choose action sequences with…