论文标题
深色倒数级：通过教师到学生的知识转移来提高图形跨度的自定位网络
Dark Reciprocal-Rank: Boosting Graph-Convolutional Self-Localization Network via Teacher-to-student Knowledge Transfer
论文作者
论文摘要
在视觉机器人自定位中，基于图形的场景表示和匹配最近吸引了研究兴趣，作为自定位的强大和歧视方法。尽管有效，但它们的计算和存储成本不能很好地扩展到大型环境。为了减轻这个问题，我们将自定位化为图形分类问题，并尝试将图形卷积神经网络（GCN）用作图形分类引擎。一种直接的方法是使用最新的自定位系统使用的视觉特征描述符，直接作为图节点特征。但是，它们在原始的自定位系统中的卓越性能不一定在基于GCN的自定位中复制。为了解决这个问题，我们基于等级匹配介绍了一种新颖的教师知识转移方案，在该方案中，通过现出现成的最先进的教师自定位模型的相互量表向量输出被用作传输的黑暗知识。实验表明，所提出的图形跨度自定位网络可以显着超过最先进的自定位系统以及教师分类器。
In visual robot self-localization, graph-based scene representation and matching have recently attracted research interest as robust and discriminative methods for selflocalization. Although effective, their computational and storage costs do not scale well to large-size environments. To alleviate this problem, we formulate self-localization as a graph classification problem and attempt to use the graph convolutional neural network (GCN) as a graph classification engine. A straightforward approach is to use visual feature descriptors that are employed by state-of-the-art self-localization systems, directly as graph node features. However, their superior performance in the original self-localization system may not necessarily be replicated in GCN-based self-localization. To address this issue, we introduce a novel teacher-to-student knowledge-transfer scheme based on rank matching, in which the reciprocal-rank vector output by an off-the-shelf state-of-the-art teacher self-localization model is used as the dark knowledge to transfer. Experiments indicate that the proposed graph-convolutional self-localization network can significantly outperform state-of-the-art self-localization systems, as well as the teacher classifier.
