12 citations · 43 across the 10 of their papers we have counts for
11 papers
Heterogeneous Graph-based Trajectory Prediction using Local Map Context and Social Interactions
Daniel Grimm, Maximilian Zipfl, Felix Hertlein +7
Precisely predicting the future trajectories of surrounding traffic participants is a crucial but challenging problem in autonomous driving, due to complex interactions between tra…
Relation-based Motion Prediction using Traffic Scene Graphs
Maximilian Zipfl, Felix Hertlein, Achim Rettinger +5
Representing relevant information of a traffic scene and understanding its environment is crucial for the success of autonomous driving. Modeling the surrounding of an autonomous c…
Context-driven Visual Object Recognition based on Knowledge Graphs
Sebastian Monka, Lavdim Halilaj, Achim Rettinger
Current deep learning methods for object recognition are purely data-driven and require a large number of training samples to achieve good results. Due to their sole dependence on…
Signing the Supermask: Keep, Hide, Invert
Nils Koster, Oliver Grothe, Achim Rettinger
The exponential growth in numbers of parameters of neural networks over the past years has been accompanied by an increase in performance across several fields. However, due to the…
A Survey on Visual Transfer Learning using Knowledge Graphs
Sebastian Monka, Lavdim Halilaj, Achim Rettinger
Recent approaches of computer vision utilize deep learning methods as they perform quite well if training and testing domains follow the same underlying data distribution. However,…
Learning Visual Models using a Knowledge Graph as a Trainer
Sebastian Monka, Lavdim Halilaj, Stefan Schmid +1
Traditional computer vision approaches, based on neural networks (NN), are typically trained on a large amount of image data. By minimizing the cross-entropy loss between a predict…
Towards Learning Cross-Modal Perception-Trace Models
Achim Rettinger, Viktoria Bogdanova, Philipp Niemann
Representation learning is a key element of state-of-the-art deep learning approaches. It enables to transform raw data into structured vector space embeddings. Such embeddings are…
Which Knowledge Graph Is Best for Me?
Michael Färber, Achim Rettinger
In recent years, DBpedia, Freebase, OpenCyc, Wikidata, and YAGO have been published as noteworthy large, cross-domain, and freely available knowledge graphs. Although extensively i…
Linking Tweets with Monolingual and Cross-Lingual News using Transformed Word Embeddings
Aditya Mogadala, Dominik Jung, Achim Rettinger
Social media platforms have grown into an important medium to spread information about an event published by the traditional media, such as news articles. Grouping such diverse sou…
Describing Natural Images Containing Novel Objects with Knowledge Guided Assitance
Aditya Mogadala, Umanga Bista, Lexing Xie +1
Images in the wild encapsulate rich knowledge about varied abstract concepts and cannot be sufficiently described with models built only using image-caption pairs containing select…
Knowledge Fusion via Embeddings from Text, Knowledge Graphs, and Images
Steffen Thoma, Achim Rettinger, Fabian Both
We present a baseline approach for cross-modal knowledge fusion. Different basic fusion methods are evaluated on existing embedding approaches to show the potential of joining know…