6 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…
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.…
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…
Enhancing Circuit Trainability with Selective Gate Activation Strategy
Jeihee Cho, Junyong Lee, Daniel Justice +1
Hybrid quantum-classical computing relies heavily on Variational Quantum Algorithms (VQAs) to tackle challenges in diverse fields like quantum chemistry and machine learning. Howev…
Q-MAML: Quantum Model-Agnostic Meta-Learning for Variational Quantum Algorithms
Junyong Lee, JeiHee Cho, Shiho Kim
In the Noisy Intermediate-Scale Quantum (NISQ) era, using variational quantum algorithms (VQAs) to solve optimization problems has become a key application. However, these algorith…