5 papers · 1 filter
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…
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…
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…
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…