  Autonomous robots frequently need to detect "interesting" scenes to decide on
further exploration, or to decide which data to share for cooperation. These
scenarios often require fast deployment with little or no training data. Prior
work considers "interestingness" based on data from the same distribution.
Instead, we propose to develop a method that automatically adapts online to the
environment to report interesting scenes quickly. To address this problem, we
develop a novel translation-invariant visual memory and design a three-stage
architecture for long-term, short-term, and online learning, which enables the
system to learn human-like experience, environmental knowledge, and online
adaption, respectively. With this system, we achieve an average of 20% higher
accuracy than the state-of-the-art unsupervised methods in a subterranean
tunnel environment. We show comparable performance to supervised methods for
robot exploration scenarios showing the efficacy of our approach. We expect
that the presented method will play an important role in the robotic
interestingness recognition exploration tasks.
