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Explainable Query Refinement for Human Machine Teaming

Award Information
Agency: Department of Defense
Branch: Special Operations Command
Contract: H9240519P0004
Agency Tracking Number: S18B-001-0010
Amount: $148,797.48
Phase: Phase I
Program: STTR
Solicitation Topic Code: SOCOM18B-001
Solicitation Number: 18.B
Timeline
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2018-12-11
Award End Date (Contract End Date): 2019-06-11
Small Business Information
28 Corporate Drive
Clifton Park, NY 12065
United States
DUNS: 010926207
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Arslan Basharat
 Technical Leader
 (518) 881-4906
 arslan.basharat@kitware.com
Business Contact
 Wayne Durr
Phone: (518) 881-4925
Email: proposals@kitware.com
Research Institution
 Rochester Institute of Technology
 Dr. Andreas Savakis Dr. Andreas Savakis
 
1 Lomb Memorial Drive
Rochester, NY 14623
United States

 (585) 475-5651
 Nonprofit College or University
Abstract

The Intelligence, Surveillance and Reconnaissance (ISR) analysts have a challenging task to extract useful information from huge volumes of data from various sources like Full Motion Video (FMV), Wide Area Motion Imagery (WAMI), satellite imagery, Synthetic Aperture Radar (SAR), and others. Modern Machine Learning (ML) algorithms based on deep learning have greatly advanced computer vision, speech recognition, and other Artificial Intelligence (AI) areas by achieving state of the art performance in many tasks. There is a great need of Human Machine Teaming (HMT) tools to assist such users by making the exploitation process more efficient through automation, while providing user the insight into the automation to establish trust. To address these challenges Kitware, Inc. has partnered with world-renowned computer vision researchers from Rochester Institute of Technology (RIT). We propose a new concept for the analyst test bed where the goal is to improve HMT through iterative query refinement and algorithm explanation features providing insight into the ML algorithms. We will build upon our experience and existing tools and provide image and video retrieval capability through Interactive Query Refinement (IQR) with new features providing explanations about the ML algorithms output.

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

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