Convolutional Block Attention

Convolutional Block Attention Modules (CBAMs) are attention mechanisms designed to improve the performance of convolutional neural networks (CNNs) by selectively focusing on the most informative features within an image. Current research focuses on integrating CBAMs into various CNN architectures, including ResNet, DenseNet, YOLO, and U-Net, for applications ranging from medical image analysis (e.g., lung nodule detection, skin lesion segmentation) to object detection in satellite imagery and manufacturing defect identification. The effectiveness of CBAMs in enhancing feature extraction and improving accuracy across diverse tasks highlights their significance in advancing computer vision and related fields.