In this paper, we aim to solve the problem of interesting scene prediction for mobile robots. This area is currently under explored but is crucial for many practical applications such as autonomous exploration and decision making. First, we expect a robot to detect novel and interesting scenes in unknown environments and lose interests over time after repeatedly observing similar objects. Second, we expect the robots to learn from unbalanced data in a short time, as the robots normally only know the uninteresting scenes before they are deployed. Inspired by those industrial demands, we first propose a novel translation-invariant visual memory for recalling and identifying interesting scenes, then design a three-stage architecture of long-term, short-term, and online learning for human-like experience, environmental knowledge, and online adaption, respectively. It is demonstrated that our approach is able to learn online and find interesting scenes for practical exploration tasks. It also achieves a much higher accuracy than the state-of-the-art algorithm on very challenging robotic interestingness prediction datasets.

该研究论文探讨了面向移动机器人的有趣场景预测问题，通过使用新颖的、平移不变的视觉记忆来提高模型的准确性，并设计了长期、短期和在线学习三个阶段的系统结构，以实现人类化体验和在线的应用适应性。最终，在多个有挑战性的机器人数据集上，我们的方法达到了比现有算法更高的精度。

基于非监督在线学习的机器人趣味视觉记忆