Omni-Modeler: Rapid Adaptive Visual Recognition with Dynamic Learning

Abstract

Deep neural network (DNN) image classification has grown rapidly as a general pattern detection tool for an extremely diverse set of applications; yet dataset accessibility remains a major limiting factor for many applications. This paper presents a novel dynamic learning approach to leverage pretrained knowledge to novel image spaces in the effort to extend the algorithm knowledge domain and reduce dataset collection requirements. The proposed Omni-Modeler generates a dynamic knowledge set by reshaping known concepts to create dynamic representation models of unknown concepts. The Omni-Modeler embeds images with a pretrained DNN and formulates compressed language encoder. The language encoded feature space is then used to rapidly generate a dynamic dictionary of concept appearance models. The results of this study demonstrate the Omni-Modeler capability to rapidly adapt across a range of image types enabling the usage of dynamically learning image classification with limited data availability.