CBAM

CBAM, or Convolutional Block Attention Module, is a technique used in machine learning to improve how neural networks process images. It works by allowing the network to focus on the most important parts of an image, both "where" the key details are (spatial attention) and "what" features matter most (channel attention). By doing so, CBAM helps the model better recognize patterns and details, leading to improved accuracy and efficiency in tasks like image classification and object detection. It's like giving the network a spotlight to highlight relevant information while dimming less important areas.