7 papers
IMFACT: Counterfactual Explanations for Time Series via Intrinsic Mode Function Substitution
Udo Schlegel, Julian Rakuschek, Thomas Seidl +3
Oscillatory signals, such as vibration, carry class-discriminative information in specific frequency bands; perturbing them in raw feature space for counterfactual analysis easily…
What-If Explanations Over Time: Counterfactuals for Time Series Classification
Udo Schlegel, Thomas Seidl
Counterfactual explanations emerge as a powerful approach in explainable AI, providing what-if scenarios that reveal how minimal changes to an input time series can alter the model…
Unveiling the "Fairness Seesaw": Discovering and Mitigating Gender and Race Bias in Vision-Language Models
Jian Lan, Udo Schlegel, Tanveer Hannan +3
Although Vision-Language Models (VLMs) have achieved remarkable success, the knowledge mechanisms underlying their social biases remain a black box, where fairness- and ethics-rela…
PRSM: A Measure to Evaluate CLIP's Robustness Against Paraphrases
Udo Schlegel, Franziska Weeber, Jian Lan +1
Contrastive Language-Image Pre-training (CLIP) is a widely used multimodal model that aligns text and image representations through large-scale training. While it performs strongly…
Human Uncertainty-Aware Data Selection and Automatic Labeling in Visual Question Answering
Jian Lan, Zhicheng Liu, Udo Schlegel +5
Large vision-language models (VLMs) achieve strong performance in Visual Question Answering but still rely heavily on supervised fine-tuning (SFT) with massive labeled datasets, wh…
Towards Explainable Deep Clustering for Time Series Data
Udo Schlegel, Gabriel Marques Tavares, Thomas Seidl
Deep clustering uncovers hidden patterns and groups in complex time series data, yet its opaque decision-making limits use in safety-critical settings. This survey offers a structu…