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The Award database is continually updated throughout the year. As a result, data for FY22 is not expected to be complete until September, 2023.
Download all SBIR.gov award data either with award abstracts (290MB)
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A data dictionary and additional information is located on the Data Resource Page. Files are refreshed monthly.
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
Algorithms for Look-down Infrared Target ExploitationSBC: 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
Adaptive camera to display mappings using computer visionSBC: POLAR RAIN, INC. Topic: N/A
The video surveillance industry is experiencing dramatic change with the move from analog to digital video. Command centers need to have coordinated viewing of multiple camera feeds at one time, and the ability to switch automatically between feeds and display relevant patterns. Conventional security control rooms include a bank of monitors connected through a switch to an array of security camera ...STTR Phase I 2006 Department of Homeland Security
SHAPE-BASED GENERALIZATION BOUNDS FOR DEEP LEARNINGSBC: GEOMETRIC DATA ANALYTICS Topic: NGA20A001
We propose to develop a theoretical understanding of the relationship between intrinsic geometric structure in both training and latent data and characteristics of functions learned from that data for deep neural network (DNN) architectures. Along the way we propose to also understand the structure of the neural networks that are best trained on a given data set. Both of these theories will lead t ...STTR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
Bounding generalization risk for Deep Neural NetworksSBC: Euler Scientific Topic: NGA20A001
Deep Neural Networks have become ubiquitous in the modern analysis of voluminous datasets with geometric symmetries. In the field of Particle Physics, experiments such as DUNE require the detection of particle signatures interacting within the detector, with analyses of over a billion 3D event images per channel each year; with typical setups containing over 150,000 different channels. In an ...STTR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency