I am happy to announce that I will give a talk at #MathPsych about "Using Large Language Models to Automate the Analysis of Verbal Reports". The talk is on Saturday in the Language&AI slot. We leverage state-of-the-art machine learning to automatically analyse verbal reports.

Robust 3D Gaussian Splatting for Novel View Synthesis in Presence of Distractors " Even with careful scene capture, the recordings often contain dynamics, such as lighting changes, moving shadows, or any unforeseeable moving objects or persons, e.g., tourists near a captured

Excited to present our paper "Robust 3D Gaussian Splatting for Novel View Synthesis in Presence of Distractors" at GCPR. TL;DR: We leverage object aware semantic segmentation masks to ignore distractors during training. Projekt: paulungermann.github.io/Robust3DGaussi…

Check out "Robust 3D Gaussian Splatting"! We improve NVS quality by removing artifacts from dynamic objects through object-aware segmentation masks. paulungermann.github.io/Robust3DGaussi… Great work by our stellar MA student Paul Ungermann A. Ettenhofer advised by Barbara Roessle. #GCPR24

I am happy to present our work on Robust Gaussian Splatting at #GCPR2024. We improve novel view synthesis quality by removing artifacts from dynamic objects through object-aware segmentation masks. Our poster session is on Wednesday 4.30-5.30 pm.

🚨 New preprint alert! Our machine-learning toolkit is now available! Integrated with jsPsych, this tool collects, transcribes, and analyzes verbal-report data in real time 🎤, supporting both online and lab-based studies. 👉 Read the full preprint here: osf.io/preprints/psya…