KI als Schlüsseltechnologie für Mustererkennung und Situationsbewusstsein - Wie erhält das Fahrzeug das erforderliche Wissen?

Zur Umfeldwahrnehmung mit Kameras und anderen Sensoren werden intelligente und selbstlernende Verfahren als Schlüsseltechnologie angesehen. Entsprechende Trainingsdaten vorausgesetzt, können mit Deep Learning und Neuronalen Netzen Fahrzeuge und andere Verkehrsobjekte bspw. in Kamerabildern detektiert, verortet und prädiziert - sowie in einen verkehrlichen Gesamtkontext gebracht werden. Zudem ist dies eine Grundlage zur Bestimmung der Eigenposition relativ zum Verkehrsgeschehen und unabhängig von störanfälligen Quellen wie Satellitennavigation. Der Vortrag ist ein Auszug aus den aktuellen Arbeiten des DLR im Rahmen der KI-Projektfamilie aus der VDA-Leitinitiative Automatisiertes Fahren. Thematisiert wird die Forschungsfrage, wie KI-Systeme effizient und möglichst vollständig mit dem erforderlichen Wissen ausgestattet werden können, um sie zur Umfeldwahrnehmung für das automatisierte Fahren einsetzen zu können

Knowledge Graph Transfer Network for Few-Shot Recognition

Few-shot learning aims to learn novel categories from very few samples given some base categories with sufficient training samples. The main challenge of this task is the novel categories are prone to dominated by color, texture, shape of the object or background context (namely specificity), which are distinct for the given few training samples but not common for the corresponding categories (see Figure 1). Fortunately, we find that transferring information of the correlated based categories can help learn the novel concepts and thus avoid the novel concept being dominated by the specificity. Besides, incorporating semantic correlations among different categories can effectively regularize this information transfer. In this work, we represent the semantic correlations in the form of structured knowledge graph and integrate this graph into deep neural networks to promote few-shot learning by a novel Knowledge Graph Transfer Network (KGTN). Specifically, by initializing each node with the classifier weight of the corresponding category, a propagation mechanism is learned to adaptively propagate node message through the graph to explore node interaction and transfer classifier information of the base categories to those of the novel ones. Extensive experiments on the ImageNet dataset show significant performance improvement compared with current leading competitors. Furthermore, we construct an ImageNet-6K dataset that covers larger scale categories, i.e, 6,000 categories, and experiments on this dataset further demonstrate the effectiveness of our proposed model.Comment: accepted by AAAI 2020 as oral pape

Universal-RCNN: Universal Object Detector via Transferable Graph R-CNN

The dominant object detection approaches treat each dataset separately and fit towards a specific domain, which cannot adapt to other domains without extensive retraining. In this paper, we address the problem of designing a universal object detection model that exploits diverse category granularity from multiple domains and predict all kinds of categories in one system. Existing works treat this problem by integrating multiple detection branches upon one shared backbone network. However, this paradigm overlooks the crucial semantic correlations between multiple domains, such as categories hierarchy, visual similarity, and linguistic relationship. To address these drawbacks, we present a novel universal object detector called Universal-RCNN that incorporates graph transfer learning for propagating relevant semantic information across multiple datasets to reach semantic coherency. Specifically, we first generate a global semantic pool by integrating all high-level semantic representation of all the categories. Then an Intra-Domain Reasoning Module learns and propagates the sparse graph representation within one dataset guided by a spatial-aware GCN. Finally, an InterDomain Transfer Module is proposed to exploit diverse transfer dependencies across all domains and enhance the regional feature representation by attending and transferring semantic contexts globally. Extensive experiments demonstrate that the proposed method significantly outperforms multiple-branch models and achieves the state-of-the-art results on multiple object detection benchmarks (mAP: 49.1% on COCO).Comment: Accepted by AAAI2