Minh Kha Do — accepted AI-conference papers

2026

Spectrally Distilled Representations Aligned with Instruction-Augmented LLMs for Satellite Imagery

Minh Kha Do, Wei Xiang, Kang Han, Di Wu, Khoa Phan, Yi-Ping Phoebe Chen +2

CVPR 2026

Vision-language foundation models (VLFMs) promise zero-shot and retrieval understanding for Earth observation. While operational satellite systems often lack full multi-spectral coverage, making RGB-only inference highly desirable for scalable deployment, the adoption of VLFMs for satellite imagery

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2025

RobSense: A Robust Multi-modal Foundation Model for Remote Sensing with Static, Temporal, and Incomplete Data Adaptability

Minh Kha Do, Kang Han, Phu Lai, Khoa T. Phan, Wei Xiang

CVPR 2025poster

Foundation models for remote sensing have garnered increasing attention for their strong performance across various observation tasks. However, current models lack robustness in managing diverse input types and handling incomplete data in downstream tasks. In this paper, we propose RobSense, a robus…

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