6 citations · 7 across the 12 of their papers we have counts for
12 papers
G-MAD: A Game-Based Data Generation Framework for Multi-View RGB-T Aerial Object Detection
Yechan Kim, JongHyun Park, Dongho Yoon +2
This work introduces G-MAD, an open-source framework that uses Arma3 to generate synchronized multi-view RGB-T data for aerial object detection. G-MAD addresses key limitations of…
BCS superconductivity in the presence of wave dark matter
Yechan Kim, Hye-Sung Lee, Jiheon Lee +1
In the established era of dark matter, condensed matter Hamiltonians-including those of superconductors-may require extension to account for the surrounding Galactic environment. W…
Unlocking Robust Semantic Segmentation Performance via Label-only Elastic Deformations against Implicit Label Noise
Yechan Kim, Dongho Yoon, Younkwan Lee +7
While previous studies on image segmentation focus on handling severe (or explicit) label noise, real-world datasets also exhibit subtle (or implicit) label imperfections. These ar…
NSegment : Label-specific Deformations for Remote Sensing Image Segmentation
Yechan Kim, DongHo Yoon, SooYeon Kim +1
Labeling errors in remote sensing (RS) image segmentation datasets often remain implicit and subtle due to ambiguous class boundaries, mixed pixels, shadows, complex terrain featur…
Oscillating scalar potential and its implications for cosmic neutrino background searches
Yechan Kim, Hye-Sung Lee
We propose a novel mechanism in which an external oscillatory wave modulates the mass-squared term of a scalar potential, periodically switching its sign. As a result of this "pote…
Dark gauge-mediated supersymmetry breaking with a massless dark photon
Brian Batell, Yechan Kim, Hye-Sung Lee +1
We study dark gauge-mediated supersymmetry breaking (dark GMSB) in a theory with a new unbroken local symmetry and massless dark photon. Messenger fields charged under b…
NBBOX: Noisy Bounding Box Improves Remote Sensing Object Detection
Yechan Kim, SooYeon Kim, Moongu Jeon
Data augmentation has shown significant advancements in computer vision to improve model performance over the years, particularly in scenarios with limited and insufficient data. C…
Investigating Long-term Training for Remote Sensing Object Detection
JongHyun Park, Yechan Kim, Moongu Jeon
Recently, numerous methods have achieved impressive performance in remote sensing object detection, relying on convolution or transformer architectures. Such detectors typically ha…
Refined approaches in second leptogenesis for the baryon-lepton asymmetry discrepancy
YeolLin ChoeJo, Kazuki Enomoto, Yechan Kim +1
The temperature-dependent mass of the heavy neutrino can lead to the second leptogenesis occurring below the electroweak scale, potentially explaining the large discrepancy between…
Second leptogenesis: Unraveling the baryon-lepton asymmetry discrepancy
YeolLin ChoeJo, Kazuki Enomoto, Yechan Kim +1
We propose a novel scenario to explain the matter-antimatter asymmetry by twofold leptogenesis, wherein heavy Majorana neutrinos exhibit temperature-dependent masses and engage in…
Dirac-Majorana neutrino type oscillation induced by a wave dark matter
YeolLin ChoeJo, Yechan Kim, Hye-Sung Lee
Some properties of a neutrino may differ significantly depending on whether it is Dirac or Majorana type. The type is determined by the relative size of Dirac and Majorana masses,…
Imbalanced Image Classification with Complement Cross Entropy
Yechan Kim, Younkwan Lee, Moongu Jeon
Recently, deep learning models have achieved great success in computer vision applications, relying on large-scale class-balanced datasets. However, imbalanced class distributions…