3 papers
cs.CV2026
Importance-Aware Low-Rank Distillation of Diffusion Transformers
Denis Zavadski, Sebastian Heid, Damjan Kalšan +2
Diffusion Transformers (DiTs) have emerged as a dominant architecture for high-quality text-to-image generation, yet their scale poses challenges for efficient deployment. While tr…
cs.CV2025
A Framework for Low-Effort Training Data Generation for Urban Semantic Segmentation
Damjan Kalšan, Denis Zavadski, Tim Küchler +3
Synthetic datasets are widely used for training urban scene recognition models, but even highly realistic renderings show a noticeable gap to real imagery. This gap is particularly…
cs.CV2024
PrimeDepth: Efficient Monocular Depth Estimation with a Stable Diffusion Preimage
Denis Zavadski, Damjan Kalšan, Carsten Rother
This work addresses the task of zero-shot monocular depth estimation. A recent advance in this field has been the idea of utilising Text-to-Image foundation models, such as Stable…