Low Level Feature

Low-level features, representing basic image or signal characteristics like texture, intensity, or edge information, are crucial for various machine learning tasks. Current research focuses on effectively integrating these features with higher-level semantic information, often employing architectures like Siamese networks, transformers, and autoencoders, and exploring techniques such as attention mechanisms and feature selection to optimize performance and efficiency. This work is significant because improved utilization of low-level features leads to more robust and efficient models across diverse applications, including medical image analysis, object detection, and brain-computer interfaces.