We introduce MAESTRO, a tailored adaptation of the Masked Autoencoder (MAE) framework that effectively orchestrates the use of multimodal, multitemporal, and multispectral Earth Observation (EO) data.

Abstract: In consumer electronics manufacturing, deep neural networks (DNNs) are increasingly employed for anomaly detection, yet current research predominantly emphasizes model accuracy at the ...

Abstract: The increasing complexity of Analog/Mixed-Signal (AMS) schematics has been posing significant challenges in structure recognition, particularly in the intellectual property (IP) industry, ...