A Spatial Attention Module (SAM) is a type of module used for spatial attention in Convolutional Neural Networks (CNNs). The SAM generates a spatial attention map by utilizing the spatial relationship of different features. This type of attention is different from the channel attention, which focuses on identifying informative channels in the input.
What is Spatial Attention?
Spatial attention is a mechanism that allows CNNs to focus on the most informative parts of the input image. This is especially relevant in tasks where certain regions of the input image contain more relevant information for the task at hand. Spatial attention modules allow the CNN to selectively amplify the relevant information and suppress the irrelevant information.
How Does a Spatial Attention Module Work?
To compute the spatial attention map, a SAM uses two pooling operations. The first pooling operation is an average pooling operation that computes the average value of each feature across the channel. The second pooling operation is a max pooling operation that computes the maximum value of each feature across the channel. These two pooled feature maps are then concatenated and fed into a standard convolutional layer with a 7x7 filter size, resulting in a 2D spatial attention map.
The formula for computing the spatial attention map is:
Ms(F) = σ(f7x7([AvgPool(F); MaxPool(F)]))
In the above formula, Ms denotes the spatial attention map, F denotes the input feature map, σ denotes the sigmoid function, f7x7 represents a convolution operation with a 7x7 filter size, AvgPool and MaxPool denote the average pooling and max pooling operations, respectively, and [ ] denotes concatenation.
Why Use a Spatial Attention Module?
The main advantage of using a spatial attention module is its ability to selectively amplify the relevant features and suppress the irrelevant features. This allows the CNN to focus on the most informative parts of the input image, leading to better task performance. Additionally, the spatial attention module is computationally efficient, as it only uses two pooling operations and a single convolutional layer.
Another advantage of using a SAM is its interpretability. The spatial attention map generated by the SAM highlights the most informative parts of the input image, allowing us to understand which parts of the image the CNN is focusing on for the task at hand.
Applications of Spatial Attention Modules
Spatial attention modules have been successfully applied to various computer vision tasks, including:
- Object Detection: In object detection tasks, spatial attention can be used to selectively amplify the most informative regions of the input image, leading to better object detection performance.
- Semantic Segmentation: In semantic segmentation tasks, spatial attention can be used to selectively amplify the regions of the input image that correspond to the object classes of interest.
- Image Captioning: In image captioning tasks, spatial attention can be used to selectively attend to different parts of the input image while generating the image caption, leading to more accurate and descriptive captions.
A Spatial Attention Module is a module for spatial attention in convolutional neural networks that selectively amplifies the most informative parts of the input image. The SAM computes a spatial attention map by utilizing the spatial relationship of features, and it is computationally efficient and interpretable. Spatial attention modules have been successfully applied to various computer vision tasks, including object detection, semantic segmentation, and image captioning.
