论文标题
通过暹罗网络使用学习的视觉提示的一击对象本地化
One-Shot Object Localization Using Learnt Visual Cues via Siamese Networks
论文作者
论文摘要
可以在新颖和非结构化环境中操作的机器人必须能够识别新的，以前看不见的对象。在这项工作中，使用视觉提示来指定一个新的感兴趣对象，该对象必须定位在新环境中。配备暹罗网络的端到端神经网络用于学习提示，推断感兴趣的对象，然后将其本地化在新环境中。我们表明，模拟的机器人可以挑选由激光指针指向的小说对象。我们还评估了源自Omniglot手写字符数据集和玩具的小数据集的数据集上所提出的方法的性能。
A robot that can operate in novel and unstructured environments must be capable of recognizing new, previously unseen, objects. In this work, a visual cue is used to specify a novel object of interest which must be localized in new environments. An end-to-end neural network equipped with a Siamese network is used to learn the cue, infer the object of interest, and then to localize it in new environments. We show that a simulated robot can pick-and-place novel objects pointed to by a laser pointer. We also evaluate the performance of the proposed approach on a dataset derived from the Omniglot handwritten character dataset and on a small dataset of toys.
