16 citations · 33 across the 21 of their papers we have counts for
25 papers
xWhyL: Causal Interactive Learning
Nicholas Tagliapietra, Florian Peter Busch, Moritz Willig +4
Explanations are central to causal reasoning, and cognitive science has long established that the human drive to explain is itself a mechanism for learning about causality. Despite…
Constraint-Guided Enterprise Data Mapping with Large Language Models
Sebastian Monka, Pramod Anantharam, Thien Vo Minh +1
Enterprise entity alignment must handle semi-structured records, implicit attributes, and unit or granularity mismatches. Manual matching is still common in practice, but does not…
CausalSteward: An Agentic Divide-Conquer-Combine Copilot for Causal Discovery
Nicholas Tagliapietra, Gian Lorenzo Marchioni, Moritz Willig +3
Learning causal models from high-dimensional data is a significant challenge, particularly in real-world settings where violations of core assumptions lead to causal identifiabilit…
GenAU: Language-Grounded Industrial Anomaly Understanding with Vision-Language Models
Hongkuan Zhou, Tristan Rehm, Nadeem Nazer +3
Industrial inspection requires more than binary anomaly detection: a practical system should determine whether an anomaly exists, localize the defective region, identify the defect…
StableRCA: Robust Graph-Agnostic Mechanism-Level Root Cause Analysis
Xiaoyu Lin, Nicholas Tagliapietra, Kehan Li +2
Root-Cause Analysis (RCA) seeks to identify the variables responsible for abnormal system behavior in complex domains such as manufacturing, cloud computing, and healthcare. Existi…
ORCA: An End-to-End Interactive Copilot for Optimized Root Cause Analysis
Phi Nguyen Xuan, Nicholas Tagliapietra, Lavdim Halilaj +2
Causal analysis is a crucial task in many domains, including manufacturing, social science, and medicine. However, despite recent progress, the conceptual and methodological comple…
Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation
Nadeem Nazer, Hongkuan Zhou, Lavdim Halilaj +2
Recent vision-language models (VLMs) like CLIP have shown impressive anomaly detection performance under significant distribution shift by utilizing high-level semantic information…
Seeing and Knowing in the Wild: Open-domain Visual Entity Recognition with Large-scale Knowledge Graphs via Contrastive Learning
Hongkuan Zhou, Lavdim Halilaj, Sebastian Monka +5
Open-domain visual entity recognition aims to identify and link entities depicted in images to a vast and evolving set of real-world concepts, such as those found in Wikidata. Unli…
DiffSemanticFusion: Semantic Raster BEV Fusion for Autonomous Driving via Online HD Map Diffusion
Zhigang Sun, Yiru Wang, Anqing Jiang +13
Autonomous driving requires accurate scene understanding, including road geometry, traffic agents, and their semantic relationships. In online HD map generation scenarios, raster-b…
Enhancing Manufacturing Knowledge Access with LLMs and Context-aware Prompting
Sebastian Monka, Irlan Grangel-González, Stefan Schmid +4
Knowledge graphs (KGs) have transformed data management within the manufacturing industry, offering effective means for integrating disparate data sources through shared and struct…
MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning
Ylli Sadikaj, Hongkuan Zhou, Lavdim Halilaj +3
Precise optical inspection in industrial applications is crucial for minimizing scrap rates and reducing the associated costs. Besides merely detecting if a product is anomalous or…
Predicting the Road Ahead: A Knowledge Graph based Foundation Model for Scene Understanding in Autonomous Driving
Hongkuan Zhou, Stefan Schmid, Yicong Li +3
The autonomous driving field has seen remarkable advancements in various topics, such as object recognition, trajectory prediction, and motion planning. However, current approaches…
CausalMan: A physics-based simulator for large-scale causality
Nicholas Tagliapietra, Juergen Luettin, Lavdim Halilaj +3
A comprehensive understanding of causality is critical for navigating and operating within today's complex real-world systems. The absence of realistic causal models with known dat…
Robust Visual Representation Learning with Multi-modal Prior Knowledge for Image Classification Under Distribution Shift
Hongkuan Zhou, Lavdim Halilaj, Sebastian Monka +4
Despite the remarkable success of deep neural networks (DNNs) in computer vision, they fail to remain high-performing when facing distribution shifts between training and testing d…
SocialFormer: Social Interaction Modeling with Edge-enhanced Heterogeneous Graph Transformers for Trajectory Prediction
Zixu Wang, Zhigang Sun, Juergen Luettin +1
Accurate trajectory prediction is crucial for ensuring safe and efficient autonomous driving. However, most existing methods overlook complex interactions between traffic participa…
SemanticFormer: Holistic and Semantic Traffic Scene Representation for Trajectory Prediction using Knowledge Graphs
Zhigang Sun, Zixu Wang, Lavdim Halilaj +1
Trajectory prediction in autonomous driving relies on accurate representation of all relevant contexts of the driving scene, including traffic participants, road topology, traffic…
nuScenes Knowledge Graph -- A comprehensive semantic representation of traffic scenes for trajectory prediction
Leon Mlodzian, Zhigang Sun, Hendrik Berkemeyer +5
Trajectory prediction in traffic scenes involves accurately forecasting the behaviour of surrounding vehicles. To achieve this objective it is crucial to consider contextual inform…
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…
A Survey on Knowledge Graph-based Methods for Automated Driving
Juergen Luettin, Sebastian Monka, Cory Henson +1
Automated driving is one of the most active research areas in computer science. Deep learning methods have made remarkable breakthroughs in machine learning in general and in autom…
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 a Semantic Administrative Shell for Industry 4.0 Components
Irlán Grangel-González, Lavdim Halilaj, Gökhan Coskun +3
In the engineering and manufacturing domain, there is currently an atmosphere of departure to a new era of digitized production. In different regions, initiatives in these directio…