For spatial and channel -based attention for CNNs, read this paper about squeeze and excitation blocks Article Squeeze-and-Excitation Networks

Integrating attention mechanisms with convolutional neural networks (CNNs) enhances image classification performance by enabling the model to focus on the most relevant parts of an image. One approach is the Spatial Attention Mechanism, which generates an attention map by considering spatial relationships between features, highlighting "where" to focus in the feature map. This is typically implemented using pooling operations followed by convolution and sigmoid activation. Another approach is the Channel Attention Mechanism, which focuses on "which" channels to emphasize. This mechanism uses global average and max pooling, followed by fully connected layers to compute an attention map across the channel dimension. A combined method, known as the Convolutional Block Attention Module (CBAM), sequentially applies both channel and spatial attention to refine the feature maps. Additionally, the Self-Attention Mechanism, inspired by Transformer models, allows each spatial position in the feature map to attend to all other positions, capturing long-range dependencies. This is achieved through dot product attention followed by a weighted sum of features. Finally, the Dual Attention Mechanism (DAM) integrates both spatial and channel attention within the same framework to exploit dependencies across both dimensions simultaneously. By incorporating these attention mechanisms, CNNs can more effectively focus on critical features and spatial regions, leading to improved image classification performance.

A different paradigm that can be also useful are Vision Transformers (ViTs). ViTs can directly analyze relationships between any two parts of the image, enabling them to grasp the bigger picture. This self-attention mechanism empowers ViTs to understand complex interactions across the entire image.