Few-shot Object detection via Reinforcement Control of Image Simulation (FORCIS)

Project FORCIS investigates solutions to the challenging problem of few-shot object detection, in which there are insufficient observations of a high-priority target to train an automatic target recognition (ATR) algorithm using standard deep-learning approaches. FORCIS specifically investigates the usage of synthetic training data generated by simulation engines which are deliberately and dynamically parameterized to maximize utility to a downstream deep-learning algorithm. Our approach on FORCIS is to first develop a flexible simulation environment based upon a modern game-development engine. An exposed interface to this simulator allows an RL agent to control the probability distributions of a wide range of simulation parameters. The RL agent (itself a neural network) is trained to explore this high-dimensional parameter-space in order to maximize the performance of a mission-specific computer-vision “Main Task Model”. Phase II of FORCIS seeks to build upon the successful results from Phase I, broadening