The Award database is continually updated throughout the year. As a result, data for FY19 is not expected to be complete until June, 2020.
Hybrid DNN-based Transfer Learning and CNN-based Supervised Learning for Object Recognition in Multi-modal Infrared ImagerySBC: TOYON RESEARCH CORPORATION Topic: 1
On this effort Toyon Research Corp. and The Pennsylvania State University are developing deep learning-based algorithms for object recognition and new class discovery in look-down infrared (IR) imagery. Our approach involves the development of a hybrid classifier that exploits both transfer learning and semi-supervised paradigms in order to maintain good generalization accuracy, especially when li ...STTR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
SBC: Signature Research, Inc. Topic: 1
Signature Research, Inc. (SGR) and Michigan Technological University (MTU) propose a Phase I STTR effort to develop a learning algorithm which exploits the spatio-spectral characteristics inherent within IR imagery and motion imagery.Our archive of modelled and labeled data sets will allow our team to thoroughly capture the variable elements that will drive machine learning performance.The overall ...STTR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
SBC: Scalable Network Technologies, Inc. Topic: OSD12T08
The goal of the proposed work is to develop a human-centric training and assessment system for cyber situation awareness. The envisioned system will enable instructors to define training goals, design lesson plans, assign students roles in teams, and observe students performance, record events and interactions for scoring. The instructor/students can place tags (time or event) to roll back or repl ...STTR Phase II 2014 Department of DefenseOffice of the Secretary of Defense