7 papers
Summarize First, Download Later: Onboard VLMs for Bandwidth-Efficient Earth Observation
Junghwan Park, Sangcheol Sim, Woojin Cho +1
Modern Earth observation (EO) satellites carry increasingly advanced sensors that produce vast volumes of high-resolution, multispectral data, yet downlink capacity remains a criti…
ELMZip: Onboard Satellite Image Compression via Extreme Learning Machines for Efficient Downlink
Woojin Cho, Junghwan Park, Sangcheol Sim +3
The acquisition of multispectral imagery via small satellites (e.g., CubeSats) presents significant data downlink challenges due to high data volumes and restricted communication w…
Few-Shot Open-Vocabulary Remote Sensing Segmentation via Textual Inversion
Junhyuk Heo, Junghwan Park
Open-vocabulary segmentation labels arbitrary categories from a text query without per-class training, yet on remote sensing imagery it underperforms on categories it handles relia…
FROST: Training-Free Few-Shot Segmentation with Frozen Features and Nonparametric Statistics
Junghwan Park
Few-shot segmentation asks a model to delineate a target class in a query image from only a handful of annotated examples, a setting most acute in remote sensing, where labels are…
Escaping Spectral Bias without Backpropagation: Fast Implicit Neural Representations with Extreme Learning Machines
Woojin Cho, Junghwan Park
Training implicit neural representations (INRs) to capture fine-scale details typically relies on iterative backpropagation and is often hindered by spectral bias when the target e…
Basis-Oriented Low-rank Transfer for Few-Shot and Test-Time Adaptation
Junghwan Park, Woojin Cho, Junhyuk Heo +2
Adapting large pre-trained models to unseen tasks under tight data and compute budgets remains challenging. Meta-learning approaches explicitly learn good initializations, but they…