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: Affective Video Facial Analysis (AVFA) is important for advancing emotion-aware AI, yet the persistent data scarcity in AVFA presents challenges. Recently, the self-supervised learning (SSL) ...

Abstract: The paper presents a framework that utilizes Convolutional Autoencoders (CAEs) for anomaly detection in industrial settings. The framework focuses on ensuring product quality and identifying ...

Abstract: This study introduces a novel strategy for waste segregation employing Convolutional Neural Networks (CNNs) and Python programming. By harnessing CNNs’ image feature extraction capabilities, ...