探索しながら視覚的顕著性を学ぶ：RL-IACアプローチ
Exploring to learn visual saliency: The RL-IAC approach
移動ロボット上で深度ベースの物体検出器から視覚的顕著性モデルをオンライン学習し、強化学習による好奇心駆動型探索（RL-IAC）で効率的にサンプルを選んで学習時間を大幅短縮した研究。
著者: Celine Craye, Timothee Lesort, David Filliat, Jean-Francois Goudou
分類: cs.CV, cs.RO
原文アブストラクト
The problem of object localization and recognition on autonomous mobile robots is still an active topic. In this context, we tackle the problem of learning a model of visual saliency directly on a robot. This model, learned and improved on-the-fly during the robot's exploration provides an efficient tool for localizing relevant objects within their environment. The proposed approach includes two intertwined components. On the one hand, we describe a method for learning and incrementally updating a model of visual saliency from a depth-based object detector. This model of saliency can also be exploited to produce bounding box proposals around objects of interest. On the other hand, we investigate an autonomous exploration technique to efficiently learn such a saliency model. The proposed exploration, called Reinforcement Learning-Intelligent Adaptive Curiosity (RL-IAC) is able to drive the robot's exploration so that samples selected by the robot are likely to improve the current model of saliency. We then demonstrate that such a saliency model learned directly on a robot outperforms several state-of-the-art saliency techniques, and that RL-IAC can drastically decrease the required time for learning a reliable saliency model.
