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KAIZEN: Knowledge Aware Intelligent Zero-shot Explainable Multi-Modal Network

Award Information
Agency: Department of Defense
Branch: National Geospatial-Intelligence Agency
Contract: HM047621C0021
Agency Tracking Number: NGA-P1-21-07
Amount: $100,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: NGA203-005
Solicitation Number: 20.3
Solicitation Year: 2020
Award Year: 2021
Award Start Date (Proposal Award Date): 2021-06-01
Award End Date (Contract End Date): 2022-02-28
Small Business Information
15400 Calhoun Drive Suite 190
Rockville, MD 20855-2814
United States
DUNS: 161911532
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Bridget Kennedy
 (240) 599-5655
Business Contact
 Mark James
Phone: (301) 294-5221
Research Institution

Detecting relevant objects of interest in large datasets using artificial intelligence techniques is very appealing. However, most state of the art approaches use deep neural network techniques -- requiring millions of human annotated training examples. These datasets mostly come from academia and don’t transfer well to novel domains. Even with the use of smaller annotated datasets and an application of transfer learning techniques, human annotations are required, which can be impossible to obtain in limited data scenarios or when annotation time from analysts is not available. Additionally, the end user of these technologies has no way of identifying why the system made its decisions and is unable to determine how to fix the errors. A system designed to address these problems should (i) learn how to detect new objects of interest quickly; (ii) be able to detect across domains and camera view-points; and (iii) be more accessible and human understandable to an end user who is not a machine learning expert. Intelligent Automation, Inc. (IAI) propose to develop a novel mathematical model and training paradigm to build a system that creates automated annotation analytics from limited or zero training samples from new datasets and domains to build robust annotation analytics.

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

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