7 papers
From Codebooks to VLMs: Evaluating Automated Visual Discourse Analysis for Climate Change on Social Media
Katharina Prasse, Steffen Jung, Isaac Bravo +4
Social media platforms have become primary arenas for climate communication, generating millions of images and posts that - if systematically analysed - can reveal which communicat…
Fix your downsampling ASAP! Be natively more robust via Aliasing and Spectral Artifact free Pooling
Julia Grabinski, Steffen Jung, Janis Keuper +1
Convolutional Neural Networks (CNNs) are successful in various computer vision tasks. From an image and signal processing point of view, this success is counter-intuitive, as the i…
Corner Cases: How Size and Position of Objects Challenge ImageNet-Trained Models
Mishal Fatima, Steffen Jung, Margret Keuper
Backgrounds in images play a major role in contributing to spurious correlations among different data points. Owing to aesthetic preferences of humans capturing the images, dataset…
TRIX- Trading Adversarial Fairness via Mixed Adversarial Training
Tejaswini Medi, Steffen Jung, Margret Keuper
Adversarial Training (AT) is a widely adopted defense against adversarial examples. However, existing approaches typically apply a uniform training objective across all classes, ov…
FAIR-TAT: Improving Model Fairness Using Targeted Adversarial Training
Tejaswini Medi, Steffen Jung, Margret Keuper
Deep neural networks are susceptible to adversarial attacks and common corruptions, which undermine their robustness. In order to enhance model resilience against such challenges,…
Deep Learning for Climate Action: Computer Vision Analysis of Visual Narratives on X
Katharina Prasse, Marcel Kleinmann, Inken Adam +8
Climate change is one of the most pressing challenges of the 21st century, sparking widespread discourse across social media platforms. Activists, policymakers, and researchers see…