2 citations · 2 across the 6 of their papers we have counts for
7 papers
Exploratory Unstructured Data Analysis: A Formative Study and Implications for Human-AI Collaboration
Johannes Eschner, Dominik Eitler, Max Irendorfer +3
We propose a conceptual framework for exploratory data analysis of (large) unstructured data (EluDA), combining classical elements (querying, visualization) with active knowledge c…
A Matter of Time: Revealing the Structure of Time in Vision-Language Models
Nidham Tekaya, Manuela Waldner, Matthias Zeppelzauer
Large-scale vision-language models (VLMs) such as CLIP have gained popularity for their generalizable and expressive multimodal representations. By leveraging large-scale training…
Distilling Knowledge from Large Language Models: A Concept Bottleneck Model for Hate and Counter Speech Recognition
Roberto Labadie-Tamayo, Djordje Slijepčević, Xihui Chen +4
The rapid increase in hate speech on social media has exposed an unprecedented impact on society, making automated methods for detecting such content important. Unlike prior black-…
FHSTP@EXIST 2025 Benchmark: Sexism Detection with Transparent Speech Concept Bottleneck Models
Roberto Labadie-Tamayo, Adrian Jaques Böck, Djordje Slijepčević +3
Sexism has become widespread on social media and in online conversation. To help address this issue, the fifth Sexism Identification in Social Networks (EXIST) challenge is initiat…
Scalable Class-Centric Visual Interactive Labeling
Matthias Matt, Jana Sedlakova, Jürgen Bernard +2
Large unlabeled datasets demand efficient and scalable data labeling solutions, in particular when the number of instances and classes is large. This leads to significant visual sc…
Interactive Discovery and Exploration of Visual Bias in Generative Text-to-Image Models
Johannes Eschner, Roberto Labadie-Tamayo, Matthias Zeppelzauer +1
Bias in generative Text-to-Image (T2I) models is a known issue, yet systematically analyzing such models' outputs to uncover it remains challenging. We introduce the Visual Bias Ex…
cVIL: Class-Centric Visual Interactive Labeling
Matthias Matt, Matthias Zeppelzauer, Manuela Waldner
We present cVIL, a class-centric approach to visual interactive labeling, which facilitates human annotation of large and complex image data sets. cVIL uses different property meas…