Feature Map

Feature maps are intermediate representations within neural networks, crucial for image processing and other tasks. Current research focuses on improving feature map generation and utilization, exploring techniques like convolutional and attention mechanisms within architectures such as Transformers, U-Nets, and Siamese networks to enhance feature extraction and manipulation for tasks ranging from object detection and segmentation to change detection and visual question answering. These advancements aim to improve model efficiency, accuracy, and interpretability, impacting various fields including autonomous driving, medical image analysis, and remote sensing.