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Multi-Task Scale -aware Continuous and Localizable Embeddings

Award Information
Agency: Department of Defense
Branch: National Geospatial-Intelligence Agency
Contract: HM047623C0034
Agency Tracking Number: O2-1953
Amount: $999,867.85
Phase: Phase II
Program: STTR
Solicitation Topic Code: OSD22A-001
Solicitation Number: 22.A
Solicitation Year: 2022
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-09-14
Award End Date (Contract End Date): 2025-09-17
Small Business Information
1712 Route 9 Suite 300
Clifton Park, NY 12065-3104
United States
DUNS: 010926207
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Christopher Funk
 (518) 881-4933
Business Contact
 Denise Hale
Phone: (518) 836-2178
Research Institution
 University of California, Berkeley
 Nicole Hensley
2150 Shattuck Ave 10th Floor
Berkeley, CA 94704-6701
United States

 (510) 642-5861
 Nonprofit College or University

In Phase I, our team of Kitware and UC-Berkeley developed Scale-MAE by adding ground sample distance (GSD) to positional encodings, and produced a multiscale representation that achieves state-of-the art results across image classification, semantic segmentation, and object detection tasks. In Phase II, we will create a remote sensing pretraining toolkit to enable fast and easy experimentation with multiple self supervised pertaining techniques that create foundational deep neural network models applicable across NGA. The foundational networks will be tested on the tasks as Scale-MAE in Phase I, and we will also benchmark performance for key point matching. Phase II will extend our Scale-MAE work by integrating additional metadata into the network to increase accuracy by providing it with more information; we will extend the approach to handle inputs including NTM, multi-spectral, and SAR data. Finally, the downstream task networks will be transitioned into NGA SAFFIRE for integration and evaluation. This system will enable NGA to quickly train and deploy new detectors to quickly respond to shifting needs and reduce the time from the analyst’s demand for a new task capability to the execution and availability of said capability.

* Information listed above is at the time of submission. *

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