CBAM Attention

CBAM (Convolutional Block Attention Module) is a neural network attention mechanism designed to improve the performance of deep learning models by selectively focusing on the most relevant features within input data. Current research focuses on integrating CBAM into various architectures, including U-Net for image segmentation and YOLOv5 for object detection, to enhance accuracy and efficiency across diverse applications such as remote sensing, food classification, and multimodal sentiment analysis. This attention mechanism has demonstrated significant improvements in accuracy and speed across numerous tasks, leading to more robust and efficient models for real-world applications ranging from automated industrial processes to ecological monitoring.