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Novel Mathematical Foundation for Automated Annotation of Massive Image Data Sets

Description:

TECHNOLOGY AREA(S): Electronics, Information Systems, Sensors

OBJECTIVE:

This announcement seeks proposals that offer dramatic improvements in automated object detection and annotation of massive image data sets. Imaging data is being created at an extraordinary rate from many sources, both from government assets as well as private ones. Automated methods for accurate and efficient object identification and annotation are needed to fully exploit this resource. This topic is focused on new artificial intelligence (AI) methods to effectively and efficiently solve this problem.

DESCRIPTION:

Current choke points blocking optimal exploitation of the full stream of available image data include confronting widely different views (perspective, resolution, etc.) of the same or similar objects and the overwhelming amounts of human effort required for effective processing. Current manual processes requires human eyes on every image to perform detection, identification, and annotation. Current state of the art AI requires intensive human support to generate giant training sets. Further, resulting methods frequently generate rule sets that are overly fragile in that training on one object is not transferrable to the detection of another object, even though the object might strike a human as essentially the same, and thus the need for increased human review of the algorithm decisions.


NGA seeks new types of AI tools optimized for the task of object identification and annotation across diverse families of image data that are reliable, robust, not dependent on extensive training demands, are applicable to objects of interest to both government and commercial concerns, and simultaneously be parsimonious with user resources in general. In particular, we seek solutions that make AI outputs both more explainable and more "lightweight" to human users.


The focus of a successful phase 1 effort should be on explaining the mathematical foundation that will enable the significantly improved AI tools described herein. Of specific interest are novel AI constructs that are more principled and universal and less ad hoc than current technology and can be used to construct a tool that performs relevant tasks. For the purposes of this announcement "relevant tasks" are limited to object identification across view types, drawing an object bounding box, and correctly labelling the object in a text annotation. A successful Phase 1 proposal should explain how the mathematical foundation needed to build the required tools will be developed in Phase 1 and implemented in a software toolkit in Phase 2. Examples should be developed during Phase 1 and should illustrate either improved reliability or robustness over the current state of the art, as well as reducing training demands and user resources. Proposals describing AI approaches that are demonstrably at or near the current state of the art in commercial AI performance, such as on ImageNet data sets, are specifically not of interest under this topic. The foundational element of a successful proposal under this topic is exploitation of novel mathematics that will enable new and better AI approaches.


Direct to Phase 2 proposals are being accepted under this topic. A straight to phase 2 proposal should describe pre-existing mathematical foundations and illustrative examples described in the paragraph above. Phase 2 proposals should also propose a set of milestones and demonstrations that will establish the novel AI tools as a viable commercial offering.

PHASE I:

A successful Phase 1 proposal should explain how the mathematical foundation needed to build the required tools described herein will be developed in Phase 1. Examples should be developed during Phase 1 and should illustrate either improved reliability or robustness over the current state of the art, as well as reducing training demands and user resources.

PHASE II:

The performer shall implement a software toolkit based on the foundations developed in Phase I.

PHASE III:

Follow-on activities are expected to be aggressively pursued by the offeror, namely in seeking opportunities to build more capable AI algorithms based upon the new mathematical foundation. This will deliver commercial benefits in the forms of improved algorithm performance.

KEYWORDS: artificial intelligence (AI); automated object detection; annotation of massive image data sets

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