论文标题
使用图表表示交通场景的自我监督聚类
Self Supervised Clustering of Traffic Scenes using Graph Representations
论文作者
论文摘要
检查图表是否相似是一个众所周知的挑战，但这是将图表组合在一起的必不可少的挑战。我们提出了一种数据驱动的方法，以聚集自我监督的流量场景，即没有手动标签。我们利用语义场景图模型来创建流量场景的通用图嵌入，然后使用暹罗网络映射到低维嵌入空间，在该网络中执行聚类。在新型方法的培训过程中，我们增加了笛卡尔空间中现有的交通场景，以产生积极的相似性样本。这使我们能够克服重建图的挑战，同时获得描述交通场景相似性的表示形式。我们可以证明，由此产生的簇具有共同的语义特征。在交互数据集上评估了该方法。
Examining graphs for similarity is a well-known challenge, but one that is mandatory for grouping graphs together. We present a data-driven method to cluster traffic scenes that is self-supervised, i.e. without manual labelling. We leverage the semantic scene graph model to create a generic graph embedding of the traffic scene, which is then mapped to a low-dimensional embedding space using a Siamese network, in which clustering is performed. In the training process of our novel approach, we augment existing traffic scenes in the Cartesian space to generate positive similarity samples. This allows us to overcome the challenge of reconstructing a graph and at the same time obtain a representation to describe the similarity of traffic scenes. We could show, that the resulting clusters possess common semantic characteristics. The approach was evaluated on the INTERACTION dataset.
