3 papers
cs.CV2026
Concept Guidance: Precise, Training-Free Latent Control for Text-to-Image Generation
Nikolai Röhrich, Isabell Hans, Felix Krause +1
Text-to-image diffusion models have two major drawbacks that severely limit their practical utility: (1) standard models lack an intrinsic mechanism for continuous, concept-specifi…
cs.CV2026
Preserve the Hard, Regenerate the Rest: Uncertainty-Guided Synthetic Training Data Augmentation with Diffusion Models
Nikolai Röhrich, Julian GleiÃner, Ahmed H. A. Ibrahim +2
Semantic segmentation models struggle with data sparsity and rare or visually diverse regions, e.g., dense regions or small objects in aerial or autonomous mobility data. While syn…
cs.CV2025
Masked Autoencoder Self Pre-Training for Defect Detection in Microelectronics
Nikolai Röhrich, Alwin Hoffmann, Richard Nordsieck +2
While transformers have surpassed convolutional neural networks (CNNs) in various computer vision tasks, microelectronics defect detection still largely relies on CNNs. We hypothes…