论文标题
快速学习，细分良好：快速对象细分在ICUB机器人上学习
Learn Fast, Segment Well: Fast Object Segmentation Learning on the iCub Robot
论文作者
论文摘要
机器人的视觉系统根据应用程序的要求不同：它可能需要高精度或可靠性，受到有限的资源来限制，或者需要快速适应动态变化的环境。在这项工作中，我们专注于实例分割任务，并对不同的技术进行了全面的研究，这些研究允许在存在新对象或不同域的存在下调整对象分割模型。我们建议使用针对数据流入的机器人应用设计的快速实例细分学习的管道。它基于在预训练的CNN上利用混合方法进行特征提取和基于快速培训的基于内核的分类器。我们还提出了一种培训协议，该协议可以通过在数据采集期间执行特征提取来缩短培训时间。我们在两个机器人数据集上基准了提议的管道，然后将其部署在一个真实的机器人上，即iCub Humanole。为了这个目的，我们将方法调整为一个增量设置，在该设置中，机器人在线学习新颖对象。复制实验的代码在GitHub上公开可用。
The visual system of a robot has different requirements depending on the application: it may require high accuracy or reliability, be constrained by limited resources or need fast adaptation to dynamically changing environments. In this work, we focus on the instance segmentation task and provide a comprehensive study of different techniques that allow adapting an object segmentation model in presence of novel objects or different domains. We propose a pipeline for fast instance segmentation learning designed for robotic applications where data come in stream. It is based on an hybrid method leveraging on a pre-trained CNN for feature extraction and fast-to-train Kernel-based classifiers. We also propose a training protocol that allows to shorten the training time by performing feature extraction during the data acquisition. We benchmark the proposed pipeline on two robotics datasets and we deploy it on a real robot, i.e. the iCub humanoid. To this aim, we adapt our method to an incremental setting in which novel objects are learned on-line by the robot. The code to reproduce the experiments is publicly available on GitHub.
