output
20042026
most citedTesting of Detection Tools for AI-Generated Text

534 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions
Showing cs.CVShow all

6 papers · 1 filter

cs.CV2025

Geometry-Aware Video Inpainting for Joint Headset Occlusion Removal and Face Reconstruction in Social XR

Fatemeh Ghorbani Lohesara, Karen Eguiazarian, Sebastian Knorr

Head-mounted displays (HMDs) are essential for experiencing extended reality (XR) environments and observing virtual content. However, they obscure the upper part of the user's fac…

cs.CV2025★ 2 cited

Creating Sorted Grid Layouts with Gradient-based Optimization

Kai Uwe Barthel, Florian Tim Barthel, Peter Eisert +2

Visually sorted grid layouts provide an efficient method for organizing high-dimensional vectors in two-dimensional space by aligning spatial proximity with similarity relationship…

cs.CV2024

SurfaceAI: Automated creation of cohesive road surface quality datasets based on open street-level imagery

Alexandra Kapp, Edith Hoffmann, Esther Weigmann +1

This paper introduces SurfaceAI, a pipeline designed to generate comprehensive georeferenced datasets on road surface type and quality from openly available street-level imagery. T…

cs.CV2024★ 9 cited

StreetSurfaceVis: a dataset of crowdsourced street-level imagery annotated by road surface type and quality

Alexandra Kapp, Edith Hoffmann, Esther Weigmann +1

Road unevenness significantly impacts the safety and comfort of traffic participants, especially vulnerable groups such as cyclists and wheelchair users. To train models for compre…

cs.CV2022★ 2 cited

Radial Prediction Domain Adaption Classifier for the MIDOG 2022 Challenge

Jonas Annuscheit, Christian Krumnow

This paper describes our contribution to the MIDOG 2022 challenge for detecting mitotic cells. One of the major problems to be addressed in the MIDOG 2022 challenge is the robustne…

cs.CV2019

Deep Aggregation of Regional Convolutional Activations for Content Based Image Retrieval

Konstantin Schall, Kai Uwe Barthel, Nico Hezel +1

One of the key challenges of deep learning based image retrieval remains in aggregating convolutional activations into one highly representative feature vector. Ideally, this descr…