Stefan Scholz
@stefan-scholz.bsky.social
Doctoral Researcher | Data and Machine Learning Enthusiast
📣 Happy to announce the publication of our article (w @nilsweidmann.bsky.social, @friederikeq.bsky.social, @sebnagel.bsky.social, @yannistheocharis.bsky.social & Molly Roberts) on the complexity and availability of community guidelines @icwsm.bsky.social! 🔗 ojs.aaai.org/index.php/IC...
🚨🚀 Looking for a comparative dataset on social media platforms? We’re excited to launch COMPARE! This is a collaborative effort by @nilsweidmann.bsky.social , @friederikeq.bsky.social , @sebnagel.bsky.social , @yannistheocharis.bsky.social & Molly Roberts. 🧵⤵️ (1/5)
Sharing our new preprint "An Image is Worth K Topics: A Visual Structural Topic Model with Image Embeddings" with @mansmag.bsky.social @matmagnani.bsky.social Alexandra Segerberg and Nataša Sladoje. Available on ArXiv: arxiv.org/abs/2504.10004
Now in FirstView: “Improving Computer Vision Interpretability: Transparent Two-Level Classification for Complex Scenes.” @stefan-scholz.bsky.social, @nilsweidmann.bsky.social, @zacharyst.bsky.social, @keremoglu.bsky.social, and Bastian Goldlücke propose a two-level method for image classification.
Finally out in @polanalysis.bsky.social (w/ @stefan-scholz.bsky.social, @zacharyst.bsky.social, @keremoglu.bsky.social and Bastian Goldlücke): "Improving Computer Vision Interpretability: Transparent Two-Level Classification for Complex Scenes" Available #OpenAccess at doi.org/10.1017/pan....
Improving Computer Vision Interpretability: Transparent Two-Level Classification for Complex Scenes | Political Analysis | Cambridge Core
Improving Computer Vision Interpretability: Transparent Two-Level Classification for Complex Scenes
doi.org
New paper forthcoming in PA, w/ @nilsweidmann.bsky.social, @zacharyst.bsky.social, @keremoglu.bsky.social and Bastian Goldlücke! We propose a method that makes image classification more transparent by identifying which objects on images are related to the outcome. Preprint: arxiv.org/abs/2407.03786
Improving Computer Vision Interpretability: Transparent Two-level Classification for Complex Scenes
Treating images as data has become increasingly popular in political science. While existing classifiers for images reach high levels of accuracy, it is difficult to systematically assess the visual f...
arxiv.org
