Feature Extraction Module

Feature extraction modules are crucial components of many machine learning models, tasked with transforming raw data into informative representations suitable for downstream tasks like classification or super-resolution. Current research emphasizes the use of deep learning architectures, particularly convolutional neural networks (CNNs) and vision transformers (ViTs), often incorporating attention mechanisms to selectively focus on relevant features within the data. These advancements are driving improvements in diverse applications, including image and video processing, human activity recognition, and eye-tracking analysis, by enabling more accurate and efficient models. The development of lightweight and trainable feature extraction modules is a key focus, aiming to optimize performance while minimizing computational demands.