What is GALA?
The global-and-local attention (GALA) module is a mechanism used in computer vision that enables a neural network to focus on certain regions of an image more than others. GALA stands out from other attention mechanisms because it uses explicit human supervision, which improves both the network's performance and interpretability. GALA extends a squeeze-and-excitation (SE) block with a spatial attention mechanism and uses a combination of global and local attention to determine where the network should focus.
How Does GALA Work?
GALA uses an attention mask to tell the network where and on what to focus. This mask combines global and local attention and is generated in two ways:
- Global Attention: This mechanism aggregates global information by global average pooling and produces a channel-wise attention weight vector using a multilayer perceptron.
- Local Attention: This mechanism conducts two consecutive $1\times 1$ convolutions on the input to produce a positional weight map.
The outputs of the local and global pathways are combined by addition and multiplication, using learnable parameters representing channel-wise weight vectors. The resulting output is passed through a hyperbolic tangent function and multiplied with the input feature map to produce the final output.
Why is GALA Important?
GALA is important because it allows neural networks to have a better understanding of the features present in an image. By focusing on certain regions more than others, GALA is able to improve the network's accuracy and also provide insights into which features are being used to make predictions. This is especially important when dealing with complex datasets where the importance of certain features may be difficult to discern.
By using explicit human supervision, GALA is also able to improve the interpretability of the network. This means that researchers can more easily understand how the network is making predictions, which is a crucial aspect of designing and improving neural networks.
Applications of GALA
GALA can be combined with any convolutional neural network (CNN) backbone and has been used in a variety of computer vision tasks, including image classification, object detection, and segmentation. In image classification, GALA has been shown to outperform other attention mechanisms, achieving state-of-the-art performance on several benchmark datasets, including ImageNet and CIFAR-100.
In object detection and segmentation tasks, GALA has been used to improve the accuracy of existing models and to reduce the number of false positives. By focusing on the most important features within an image, GALA enables the network to better localize and identify objects of interest.
The global-and-local attention (GALA) module is an important mechanism used in computer vision that enables neural networks to focus on certain regions of an image more than others. By using explicit human supervision, GALA improves both the network's accuracy and interpretability, making it an important tool for researchers and practitioners working in computer vision. GALA can be combined with any CNN backbone and has been used in a variety of computer vision tasks, achieving state-of-the-art performance on several benchmark datasets.
