Author Name: Weiskopf, Daniel
Keyword Term: Visual Analytics
Author Name: Munz-Körner, Tanja
Author Affiliation: University of Stuttgart
1 to 4 of 4 Results
May 27, 2024 - PN 6-4
Munz-Körner, Tanja; Weiskopf, Daniel, 2024, "Supplemental Material for "Exploring Visual Quality of Multidimensional Time Series Projections"", https://doi.org/10.18419/darus-3965, DaRUS, V1
Supplemental material for our paper "Exploring visual quality of multidimensional time series projections": A video demonstrating the interactive use of our exploration system. A table containing publications using dimensionality reduction on multidimensional time series to proje... |
Jul 25, 2023 - PN 6-4
Schäfer, Noel; Tilli, Pascal; Munz-Körner, Tanja; Künzel, Sebastian; Vidyapu, Sandeep; Vu, Ngoc Thang; Weiskopf, Daniel, 2023, "Model Parameters and Evaluation Data for our Visual Analysis System for Scene-Graph-Based Visual Question Answering", https://doi.org/10.18419/darus-3597, DaRUS, V1
Pretrained model parameters and pregenerated evaluation data for our visual analysis system for scene-graph-based visual question answering (https://doi.org/10.18419/darus-3589). |
Jul 25, 2023 - PN 6-4
Munz-Körner, Tanja; Künzel, Sebastian; Weiskopf, Daniel, 2023, "Supplemental Material for "Visual-Explainable AI: The Use Case of Language Models"", https://doi.org/10.18419/darus-3456, DaRUS, V1
Supplemental material for the poster "Visual-Explainable AI: The Use Case of Language Models" published at the International Conference on Data-Integrated Simulation Science 2023. Collection of videos and images showing our interactive visualization systems for exploring language... |
Jul 25, 2023 - PN 6-4
Schäfer, Noel; Tilli, Pascal; Munz-Körner, Tanja; Künzel, Sebastian; Vidyapu, Sandeep; Vu, Ngoc Thang; Weiskopf, Daniel, 2023, "Visual Analysis System for Scene-Graph-Based Visual Question Answering", https://doi.org/10.18419/darus-3589, DaRUS, V1
Source code of our visual analysis system to explore scene-graph-based visual question answering. This approach is built on top of the state-of-the-art GraphVQA framework which was trained on the GQA dataset. Instructions on how to use our system can be found in the README. |