7 citations · 9 across the 5 of their papers we have counts for
5 papers
SpectraDINO: Modality-Conditioned Adaptation of RGB Vision Foundation Models Across Infrared Bands
Yagiz Nalcakan, Hyeongjin Ju, Incheol Park +3
Vision foundation models (VFMs) pretrained on large-scale RGB data provide strong general-purpose representations, yet infrared perception, which is essential for robotics and driv…
RPT-SR: Regional Prior attention Transformer for infrared image Super-Resolution
Youngwan Jin, Incheol Park, Yagiz Nalcakan +3
General-purpose super-resolution models, particularly Vision Transformers, have achieved remarkable success but exhibit fundamental inefficiencies in common infrared imaging scenar…
RASMD: RGB And SWIR Multispectral Driving Dataset for Robust Perception in Adverse Conditions
Youngwan Jin, Michal Kovac, Yagiz Nalcakan +4
Current autonomous driving algorithms heavily rely on the visible spectrum, which is prone to performance degradation in adverse conditions like fog, rain, snow, glare, and high co…
Pix2Next: Leveraging Vision Foundation Models for RGB to NIR Image Translation
Youngwan Jin, Incheol Park, Hanbin Song +3
This paper proposes Pix2Next, a novel image-to-image translation framework designed to address the challenge of generating high-quality Near-Infrared (NIR) images from RGB inputs.…
Monocular Vision-based Prediction of Cut-in Maneuvers with LSTM Networks
Yagiz Nalcakan, Yalin Bastanlar
Advanced driver assistance and automated driving systems should be capable of predicting and avoiding dangerous situations. This study proposes a method to predict potentially dang…